def __init__(self):
"""Genome Constructor"""
self.evaluator = FunctionSlot("Evaluator")
self.initializator = FunctionSlot("Initializator")
self.mutator = FunctionSlot("Mutator")
self.crossover = FunctionSlot("Crossover")
self.internalParams = {}
self.score = 0.0
self.fitness = 0.0
def __init__(self):
""" Particle Constructor """
self.evaluator = FunctionSlot("Evaluator")
self.position_initializator = FunctionSlot("Position Initializator")
self.velocity_initializator = FunctionSlot(" Velocity Initializator")
self.position_communicator = FunctionSlot("Position Communicator")
self.information_communicator = FunctionSlot("Information Communicator")
self.allSlots = [self.evaluator, self.position_initializator,
self.velocity_initializator, self.position_communicator, self.information_communicator]
self.internalParams = {}
self.fitness = 0.0
self.ownBestFitness = 0.0
def __init__(self):
"""Genome Constructor"""
self.evaluator = FunctionSlot("Evaluator")
self.initializator = FunctionSlot("Initializator")
self.mutator = FunctionSlot("Mutator")
self.crossover = FunctionSlot("Crossover")
###
self.list_dic={}
self.list_margin={}
self.totalmoney=0
#my_set
self.internalParams = {}
self.score = 0.0
self.fitness = 0.0
def __init__(self):
self.selector = FunctionSlot("Selector")
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.networkCompression = 9
def __init__(self, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
Util.raiseException("Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
Util.raiseException("The genome must be a GenomeBase subclass", TypeError)
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [ self.selector, self.stepCallback, self.terminationCriteria ]
self.internalParams = {}
####
#####
self.currentGeneration = 0
# GP Testing
for classes in Consts.CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d", self.nGenerations)
def __init__(self, host, port, group_name):
self.myself = None
self.groupName = group_name
self.selector = FunctionSlot("Selector")
self.setMyself(host, port)
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.networkCompression = 9
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.evaluator = FunctionSlot("Evaluator")
self.internalParams = genome.internalParams
self.multiProcessing = genome.multiProcessing
self.statted = False
self.stats = Statistics()
return
logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.evaluator = FunctionSlot("Evaluator")
self.internalParams = {}
self.multiProcessing = (False, False)
# Statistics
self.statted = False
self.stats = Statistics()
def __init__(self,topology,seed=None,interactiveMode=True):
""" Initializator of PSO """
#random seed
random.seed(seed)
#Pso type used by the particle
self.psoType = Consts.CDefPsoType
#Topology used
self.topology = topology
#Set the population size
self.setSwarmSize(Consts.CDefSwarmSize)
#Cognitive and Social Coefficients
self.C1,self.C2 = Consts.CDefCoefficients
#Time steps
self.timeSteps = Consts.CDefSteps
#Interactive Mode (True or False)
self.interactiveMode = interactiveMode
#Current step
self.currentStep = 0
#Inertia Factor Minus
self.inertiaFactorMinus = None
#Inertia coefficient
self.inertiaFactor = None
#Time initial
self.time_init = None
#Optimization type
self.minimax = Consts.minimaxType["minimize"]
#Report file adapter
self.reportAdapter = None
#Step Callback
self.stepCallback = FunctionSlot("Step Callback")
#Termination Criteria
self.terminationCriteria = FunctionSlot("Termination Criteria")
#All slots
self.allSlots = [self.stepCallback, self.terminationCriteria]
print "A PSO Engine was created, timeSteps=% d" % ( self.timeSteps, )
def __init__(self, genome):
""" The GPopulation Class creator """
logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
# Statistics
self.statted = False
self.stats = Statistics()
def __init__(self, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
Util.raiseException("The genome must be a GenomeBase subclass",
TypeError)
self.genome = genome
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [
self.selector, self.stepCallback, self.terminationCriteria
]
self.internalParams = {}
self.currentGeneration = 0
# GP Testing
for classes in Consts.CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d",
self.nGenerations)
def __init__(self):
self.myself = None
self.selector = FunctionSlot("Selector")
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.comm = MPI.COMM_WORLD
self.pid = self.comm.rank
self.best_selector = Selectors.GRankSelector
#now this is fixed
if self.pid == 0:
self.source = self.comm.size - 1
else:
self.source = self.comm.rank - 1
self.dest = (self.comm.rank +1) % (self.comm.size)
self.all_stars = None
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.evaluator = FunctionSlot("Evaluator")
self.internalParams = genome.internalParams
self.multiProcessing = genome.multiProcessing
self.statted = False
self.stats = Statistics()
return
logging.debug("New population instance, %s class genomes.",
genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.evaluator = FunctionSlot("Evaluator")
self.internalParams = {}
self.multiProcessing = (False, False)
# Statistics
self.statted = False
self.stats = Statistics()
class MPIMigrator(object):
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
migration_scheme.selector.set(Selectors.GRouletteWheel) """
def __init__(self):
self.myself = None
self.selector = FunctionSlot("Selector")
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.comm = MPI.COMM_WORLD
self.pid = self.comm.rank
self.best_selector = Selectors.GRankSelector
#now this is fixed
if self.pid == 0:
self.source = self.comm.size - 1
else:
self.source = self.comm.rank - 1
self.dest = (self.comm.rank +1) % (self.comm.size)
self.all_stars = None
def isReady(self):
""" Returns true if is time to migrate """
if self.GAEngine.getCurrentGeneration() == 0:
return False
if self.GAEngine.getCurrentGeneration() % self.nMigrationRate == 0:
return True
else:
return False
def getNumReplacement(self):
""" Return the number of individuals that will be
replaced in the migration process """
return self.nReplacement
def setNumReplacement(self, num_individuals):
""" Return the number of individuals that will be
replaced in the migration process
:param num_individuals: the number of individuals to be replaced
"""
self.nReplacement = num_individuals
def getNumIndividuals(self):
""" Return the number of individuals that will migrate
:rtype: the number of individuals to be replaced
"""
return self.nIndividuals
def setNumIndividuals(self, num_individuals):
""" Set the number of individuals that will migrate
:param num_individuals: the number of individuals
"""
self.nIndividuals = num_individuals
def setMigrationRate(self, generations):
""" Sets the generation frequency supposed to migrate
and receive individuals.
:param generations: the number of generations
"""
self.nMigrationRate = generations
def getMigrationRate(self):
""" Return the the generation frequency supposed to migrate
and receive individuals
:rtype: the number of generations
"""
return self.nMigrationRate
def setGAEngine(self, ga_engine):
""" Sets the GA Engine handler """
self.GAEngine = ga_engine
def start(self):
""" Initializes the migration scheme """
pass
def stop(self):
""" Stops the migration engine """
pass
def getGroupName(self):
""" Gets the group name
.. note:: all islands of evolution which are supposed to exchange
individuals, must have the same group name.
"""
return self.groupName
def setGroupName(self, name):
""" Sets the group name
:param name: the group name
.. note:: all islands of evolution which are supposed to exchange
individuals, must have the same group name.
"""
self.groupName = name
def select(self):
""" Pickes an individual from population using specific selection method
:rtype: an individual object
"""
if self.selector.isEmpty():
return self.GAEngine.select(popID=self.GAEngine.currentGeneration)
else:
for it in self.selector.applyFunctions(self.GAEngine.internalPop,
popID=self.GAEngine.currentGeneration):
return it
def selectPool(self, num_individuals):
""" Select num_individuals number of individuals and return a pool
:param num_individuals: the number of individuals to select
:rtype: list with individuals
"""
pool = [self.select() for _ in xrange(num_individuals)]
return pool
def gather_bests(self):
'''
Collect all the best individuals from the various populations. The
result is stored in process 0
'''
best_guy = self.best_selector(self.GAEngine.internalPop,
popID=self.GAEngine.currentGeneration)
self.all_stars = self.comm.gather(sendobj = best_guy, root = 0)
def exchange(self):
""" This is the main method, is where the individuals
are exchanged """
if not self.isReady():
return
pool_to_send = self.selectPool(self.getNumIndividuals())
pool_received = self.comm.sendrecv(sendobj=pool_to_send,
dest=self.dest,
sendtag=0,
recvobj=None,
source=self.source,
recvtag=0)
population = self.GAEngine.getPopulation()
pool = pool_received
for i in xrange(self.getNumReplacement()):
if len(pool) <= 0:
break
choice = rand_choice(pool)
pool.remove(choice)
# replace the worst
population[len(population)-1-i] = choice
self.gather_bests()
class GenomeBase:
""" GenomeBase Class - The base of all chromosome representation """
evaluator = None
""" This is the :term:`evaluation function` slot, you can add
a function with the *set* method: ::
genome.evaluator.set(eval_func)
"""
initializator = None
""" This is the initialization function of the genome, you
can change the default initializator using the function slot: ::
genome.initializator.set(Initializators.G1DListInitializatorAllele)
In this example, the initializator :func:`Initializators.G1DListInitializatorAllele`
will be used to create the initial population.
"""
mutator = None
""" This is the mutator function slot, you can change the default
mutator using the slot *set* function: ::
genome.mutator.set(Mutators.G1DListMutatorSwap)
"""
premutator = None
crossover = None
""" This is the reproduction function slot, the crossover. You
can change the default crossover method using: ::
genome.crossover.set(Crossovers.G1DListCrossoverUniform)
"""
def __init__(self):
"""Genome Constructor"""
self.evaluator = FunctionSlot("Evaluator")
self.initializator = FunctionSlot("Initializator")
self.mutator = FunctionSlot("Mutator")
self.premutator = FunctionSlot("PreMutator")
self.crossover = FunctionSlot("Crossover")
self.internalParams = {}
self.score = 0.0
self.fitness = 0.0
def getRawScore(self):
""" Get the Raw Score of the genome
:rtype: genome raw score
"""
return self.score
def getFitnessScore(self):
""" Get the Fitness Score of the genome
:rtype: genome fitness score
"""
return self.fitness
def __repr__(self):
"""String representation of Genome"""
allSlots = self.allSlots = [ self.evaluator, self.initializator,
self.mutator, self.crossover ]
ret = "- GenomeBase\n"
ret+= "\tScore:\t\t\t %.6f\n" % (self.score,)
ret+= "\tFitness:\t\t %.6f\n\n" % (self.fitness,)
ret+= "\tParams:\t\t %s\n\n" % (self.internalParams,)
for slot in allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
return ret
def setParams(self, **args):
""" Set the internal params
Example:
>>> genome.setParams(rangemin=0, rangemax=100, gauss_mu=0, gauss_sigma=1)
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param args: this params will saved in every chromosome for genetic op. use
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> genome.getParam("rangemax")
100
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def resetStats(self):
""" Clear score and fitness of genome """
self.score = 0.0
self.fitness = 0.0
def evaluate(self, **args):
""" Called to evaluate genome
:param args: this parameters will be passes to the evaluator
"""
self.resetStats()
for it in self.evaluator.applyFunctions(self, **args):
self.score += it
def initialize(self, **args):
""" Called to initialize genome
:param args: this parameters will be passed to the initializator
"""
for it in self.initializator.applyFunctions(self, **args):
pass
def mutate(self, **args):
""" Called to mutate the genome
:param args: this parameters will be passed to the mutator
:rtype: the number of mutations returned by mutation operator
"""
nmuts = 0
for it in self.mutator.applyFunctions(self, **args):
nmuts+=it
return nmuts
def premutate(self, **args):
""" Called to pre-mutate the genome
:param args: this parameters will be passed to the premutator
:rtype: the number of mutations returned by mutation operator
"""
nmuts = 0
for it in self.premutator.applyFunctions(self, **args):
nmuts+=it
return nmuts
def copy(self, g):
""" Copy the current GenomeBase to 'g'
:param g: the destination genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
g.score = self.score
g.fitness = self.fitness
g.evaluator = self.evaluator
g.initializator = self.initializator
g.mutator = self.mutator
g.premutator = self.premutator
g.crossover = self.crossover
#g.internalParams = self.internalParams.copy()
g.internalParams = self.internalParams
def clone(self):
""" Clone this GenomeBase
:rtype: the clone genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
newcopy = GenomeBase()
self.copy(newcopy)
return newcopy
class GSimpleGA:
""" GA Engine Class - The Genetic Algorithm Core
Example:
>>> ga = GSimpleGA.GSimpleGA(genome)
>>> ga.selector.set(Selectors.GRouletteWheel)
>>> ga.setGenerations(120)
>>> ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
:param genome: the :term:`Sample Genome`
:param interactiveMode: this flag enables the Interactive Mode, the default is True
:param seed: the random seed value
.. note:: if you use the same random seed, all the runs of algorithm will be the same
"""
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
ga_engine.selector.set(Selectors.GRouletteWheel) """
stepCallback = None
""" This is the :term:`step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(ga_engine):
# Here you have access to the GA Engine
return False
ga_engine.stepCallback.set(your_func)
now *"your_func"* will be called every generation.
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you must do this: ::
ga_engine.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
Now, when you run your GA, it will stop when the population converges.
There are those termination criteria functions: :func:`GSimpleGA.RawScoreCriteria`, :func:`GSimpleGA.ConvergenceCriteria`, :func:`GSimpleGA.RawStatsCriteria`, :func:`GSimpleGA.FitnessStatsCriteria`
But you can create your own termination function, this function receives
one parameter which is the GA Engine, follows an example: ::
def ConvergenceCriteria(ga_engine):
pop = ga_engine.getPopulation()
return pop[0] == pop[len(pop)-1]
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues, this function is called every
generation.
"""
def __init__(self, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
Util.raiseException("Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
Util.raiseException("The genome must be a GenomeBase subclass", TypeError)
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [ self.selector, self.stepCallback, self.terminationCriteria ]
self.internalParams = {}
self.currentGeneration = 0
# GP Testing
for classes in Consts.CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d", self.nGenerations)
def setGPMode(self, bool_value):
""" Sets the Genetic Programming mode of the GA Engine
:param bool_value: True or False
"""
self.GPMode = bool_value
def getGPMode(self):
""" Get the Genetic Programming mode of the GA Engine
:rtype: True or False
"""
return self.GPMode
def __call__(self, *args, **kwargs):
""" A method to implement a callable object
Example:
>>> ga_engine(freq_stats=10)
.. versionadded:: 0.6
The callable method.
"""
if kwargs.get("freq_stats", None):
return self.evolve(kwargs.get("freq_stats"))
else:
return self.evolve()
def setParams(self, **args):
""" Set the internal params
Example:
>>> ga.setParams(gp_terminals=['x', 'y'])
:param args: params to save
..versionaddd:: 0.6
Added the *setParams* method.
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> ga.getParam("gp_terminals")
['x', 'y']
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
..versionaddd:: 0.6
Added the *getParam* method.
"""
return self.internalParams.get(key, nvl)
def setInteractiveGeneration(self, generation):
""" Sets the generation in which the GA must enter in the
Interactive Mode
:param generation: the generation number, use "-1" to disable
.. versionadded::0.6
The *setInteractiveGeneration* method.
"""
if generation < -1:
Util.raiseException("Generation must be >= -1", ValueError)
self.interactiveGen = generation
def getInteractiveGeneration(self):
""" returns the generation in which the GA must enter in the
Interactive Mode
:rtype: the generation number or -1 if not set
.. versionadded::0.6
The *getInteractiveGeneration* method.
"""
return self.interactiveGen
def setElitismReplacement(self, numreplace):
""" Set the number of best individuals to copy to the next generation on the elitism
:param numreplace: the number of individuals
.. versionadded:: 0.6
The *setElitismReplacement* method.
"""
if numreplace < 1:
Util.raiseException("Replacement number must be >= 1", ValueError)
self.nElitismReplacement = numreplace
def setInteractiveMode(self, flag=True):
""" Enable/disable the interactive mode
:param flag: True or False
.. versionadded: 0.6
The *setInteractiveMode* method.
"""
if type(flag) != BooleanType:
Util.raiseException("Interactive Mode option must be True or False", TypeError)
self.interactiveMode = flag
def __repr__(self):
""" The string representation of the GA Engine """
ret = "- GSimpleGA\n"
ret += "\tGP Mode:\t\t %s\n" % self.getGPMode()
ret += "\tPopulation Size:\t %d\n" % (self.internalPop.popSize,)
ret += "\tGenerations:\t\t %d\n" % (self.nGenerations,)
ret += "\tCurrent Generation:\t %d\n" % (self.currentGeneration,)
ret += "\tMutation Rate:\t\t %.2f\n" % (self.pMutation,)
ret += "\tCrossover Rate:\t\t %.2f\n" % (self.pCrossover,)
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
ret += "\tElitism:\t\t %s\n" % (self.elitism,)
ret += "\tElitism Replacement:\t %d\n" % (self.nElitismReplacement,)
ret += "\tDB Adapter:\t\t %s\n" % (self.dbAdapter,)
for slot in self.allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
return ret
def setMultiProcessing(self, flag=True, full_copy=False):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
Pyevolve will automaticly check if your Python version has **multiprocessing**
support and if you have more than one single CPU core. If you don't have support
or have just only one core, Pyevolve will not use the **multiprocessing**
feature.
Pyevolve uses the **multiprocessing** to execute the evaluation function over
the individuals, so the use of this feature will make sense if you have a
truly slow evaluation function (which is commom in GAs).
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
.. warning:: Use this option only when your evaluation function is slow, so you'll
get a good tradeoff between the process communication speed and the
parallel evaluation. The use of the **multiprocessing** doesn't means
always a better performance.
.. note:: To enable the multiprocessing option, you **MUST** add the *__main__* check
on your application, otherwise, it will result in errors. See more on the
`Python Docs <http://docs.python.org/library/multiprocessing.html#multiprocessing-programming>`__
site.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
if type(flag) != BooleanType:
Util.raiseException("Multiprocessing option must be True or False", TypeError)
if type(full_copy) != BooleanType:
Util.raiseException("Multiprocessing 'full_copy' option must be True or False", TypeError)
self.internalPop.setMultiProcessing(flag, full_copy)
def setMigrationAdapter(self, migration_adapter=None):
""" Sets the Migration Adapter
.. versionadded:: 0.6
The `setMigrationAdapter` method.
"""
self.migrationAdapter = migration_adapter
if self.migrationAdapter is not None:
self.migrationAdapter.setGAEngine(self)
def setDBAdapter(self, dbadapter=None):
""" Sets the DB Adapter of the GA Engine
:param dbadapter: one of the :mod:`DBAdapters` classes instance
.. warning:: the use the of a DB Adapter can reduce the speed performance of the
Genetic Algorithm.
"""
if (dbadapter is not None) and (not isinstance(dbadapter, DBBaseAdapter)):
Util.raiseException("The DB Adapter must be a DBBaseAdapter subclass", TypeError)
self.dbAdapter = dbadapter
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def setSortType(self, sort_type):
""" Sets the sort type, Consts.sortType["raw"]/Consts.sortType["scaled"]
Example:
>>> ga_engine.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
if sort_type not in Consts.sortType.values():
Util.raiseException("sort type must be a Consts.sortType type", TypeError)
self.internalPop.sortType = sort_type
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate>1.0) or (rate<0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0", ValueError)
self.pMutation = rate
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate>1.0) or (rate<0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0", ValueError)
self.pCrossover = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1", ValueError)
self.nGenerations = num_gens
def getGenerations(self):
""" Return the number of generations to evolve
:rtype: the number of generations
.. versionadded:: 0.6
Added the *getGenerations* method
"""
return self.nGenerations
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: the Consts.minimaxType type
"""
return self.minimax
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize", TypeError)
self.minimax = mtype
def getCurrentGeneration(self):
""" Gets the current generation
:rtype: the current generation
"""
return self.currentGeneration
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != BooleanType:
Util.raiseException("Elitism option must be True or False", TypeError)
self.elitism = flag
def getDBAdapter(self):
""" Gets the DB Adapter of the GA Engine
:rtype: a instance from one of the :mod:`DBAdapters` classes
"""
return self.dbAdapter
def bestIndividual(self):
""" Returns the population best individual
:rtype: the best individual
"""
return self.internalPop.bestRaw()
def worstIndividual(self):
""" Returns the population worst individual
:rtype: the best individual
"""
return self.internalPop.worstRaw()
def __gp_catch_functions(self, prefix):
""" Internally used to catch functions with some specific prefix
as non-terminals of the GP core """
import __main__ as mod_main
function_set = {}
main_dict = mod_main.__dict__
for obj, addr in main_dict.items():
if obj[0:len(prefix)] == prefix:
try:
op_len = addr.func_code.co_argcount
except:
continue
function_set[obj] = op_len
if len(function_set) <= 0:
Util.raiseException("No function set found using function prefix '%s' !" % prefix, ValueError)
self.setParams(gp_function_set=function_set)
def initialize(self):
""" Initializes the GA Engine. Create and initialize population """
self.internalPop.create(minimax=self.minimax)
self.internalPop.initialize(ga_engine=self)
logging.debug("The GA Engine was initialized !")
def getPopulation(self):
""" Return the internal population of GA Engine
:rtype: the population (:class:`GPopulation.GPopulation`)
"""
return self.internalPop
def getStatistics(self):
""" Gets the Statistics class instance of current generation
:rtype: the statistics instance (:class:`Statistics.Statistics`)
"""
return self.internalPop.getStatistics()
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
logging.debug("Population was cloned.")
size_iterate = len(self.internalPop)
# Odd population size
if size_iterate % 2 != 0: size_iterate -= 1
crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty()
for i in xrange(0, size_iterate, 2):
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
if len(self.internalPop) % 2 != 0:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
logging.debug("Evaluating the new created population.")
newPop.evaluate()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)
def printStats(self):
""" Print generation statistics
:rtype: the printed statistics as string
.. versionchanged:: 0.6
The return of *printStats* method.
"""
percent = self.currentGeneration * 100 / float(self.nGenerations)
message = "Gen. %d (%.2f%%):" % (self.currentGeneration, percent)
logging.info(message)
print message,
sys_stdout.flush()
self.internalPop.statistics()
stat_ret = self.internalPop.printStats()
return message + stat_ret
def printTimeElapsed(self):
""" Shows the time elapsed since the begin of evolution """
total_time = time()-self.time_init
print "Total time elapsed: %.3f seconds." % total_time
return total_time
def dumpStatsDB(self):
""" Dumps the current statistics to database adapter """
logging.debug("Dumping stats to the DB Adapter")
self.internalPop.statistics()
self.dbAdapter.insert(self)
def evolve(self, freq_stats=0):
""" Do all the generations until the termination criteria, accepts
the freq_stats (default is 0) to dump statistics at n-generation
Example:
>>> ga_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats generation.
:rtype: returns the best individual of the evolution
.. versionadded:: 0.6
the return of the best individual
"""
stopFlagCallback = False
stopFlagTerminationCriteria = False
self.time_init = time()
logging.debug("Starting the DB Adapter and the Migration Adapter if any")
if self.dbAdapter: self.dbAdapter.open(self)
if self.migrationAdapter: self.migrationAdapter.start()
if self.getGPMode():
gp_function_prefix = self.getParam("gp_function_prefix")
if gp_function_prefix is not None:
self.__gp_catch_functions(gp_function_prefix)
self.initialize()
self.internalPop.evaluate()
self.internalPop.sort()
logging.debug("Starting loop over evolutionary algorithm.")
def stop_evolution(s, f):
#print signal, frame
if s == signal.SIGINT:
if self.internalPop.multiProcessing[0]:
logging.debug("CTRL-C detected, finishing evolution (stopping parallel jobs).")
self.internalPop.proc_pool.terminate()
self.internalPop.proc_pool.join()
else:
logging.debug("CTRL-C detected, finishing evolution.")
if freq_stats: print "\n\tA break was detected, you have interrupted the evolution !\n"
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
if self.dbAdapter:
logging.debug("Closing the DB Adapter")
if not (self.currentGeneration % self.dbAdapter.getStatsGenFreq() == 0):
self.dumpStatsDB()
self.dbAdapter.commitAndClose()
if self.migrationAdapter:
logging.debug("Closing the Migration Adapter")
if freq_stats: print "Stopping the migration adapter... ",
self.migrationAdapter.stop()
if freq_stats: print "done !"
if s == signal.SIGINT:
print self.bestIndividual()
exit(0)
else:
return self.bestIndividual()
signal.signal(signal.SIGINT, stop_evolution)
while True:
if self.migrationAdapter:
logging.debug("Migration adapter: exchange")
self.migrationAdapter.exchange()
self.internalPop.clearFlags()
self.internalPop.sort()
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats:
if (self.currentGeneration % freq_stats == 0) or (self.getCurrentGeneration() == 0):
self.printStats()
#print self.bestIndividual()
if self.dbAdapter:
if self.currentGeneration % self.dbAdapter.getStatsGenFreq() == 0:
self.dumpStatsDB()
if stopFlagTerminationCriteria:
logging.debug("Evolution stopped by the Termination Criteria !")
if freq_stats:
print "\n\tEvolution stopped by Termination Criteria function !\n"
break
if stopFlagCallback:
logging.debug("Evolution stopped by Step Callback function !")
if freq_stats:
print "\n\tEvolution stopped by Step Callback function !\n"
break
if self.interactiveMode:
if sys_platform[:3] == "win":
if msvcrt.kbhit():
if ord(msvcrt.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive Mode...",
logging.debug("Windows Interactive Mode key detected ! generation=%d", self.getCurrentGeneration())
from pyevolve import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##\nPress CTRL-Z to quit interactive mode." % (pyevolve.__version__,)
session_locals = { "ga_engine" : self,
"population" : self.getPopulation(),
"pyevolve" : pyevolve,
"it" : Interaction}
print
code.interact(interact_banner, local=session_locals)
if (self.getInteractiveGeneration() >= 0) and (self.getInteractiveGeneration() == self.getCurrentGeneration()):
print "Loading modules for Interactive Mode...",
logging.debug("Manual Interactive Mode key detected ! generation=%d", self.getCurrentGeneration())
from pyevolve import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##" % (pyevolve.__version__,)
session_locals = { "ga_engine" : self,
"population" : self.getPopulation(),
"pyevolve" : pyevolve,
"it" : Interaction}
print
code.interact(interact_banner, local=session_locals)
if self.step(): break
return stop_evolution(signal.SIGUSR1, None)
def select(self, **args):
""" Select one individual from population
:param args: this parameters will be sent to the selector
"""
for it in self.selector.applyFunctions(self.internalPop, **args):
return it
class GPopulation:
""" GPopulation Class - The container for the population
**Examples**
Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
>>> pop = ga_engine.getPopulation()
Get the best fitness individual
>>> bestIndividual = pop.bestFitness()
Get the best raw individual
>>> bestIndividual = pop.bestRaw()
Get the statistics from the :class:`Statistics.Statistics` instance
>>> stats = pop.getStatistics()
>>> print stats["rawMax"]
10.4
Iterate, get/set individuals
>>> for ind in pop:
>>> print ind
(...)
>>> for i in xrange(len(pop)):
>>> print pop[i]
(...)
>>> pop[10] = newGenome
>>> pop[10].fitness
12.5
:param genome: the :term:`Sample genome`
"""
def __init__(self, genome):
""" The GPopulation Class creator """
logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
# Statistics
self.statted = False
self.stats = Statistics()
def setMinimax(minimax):
""" Sets the population minimax
Example:
>>> pop.setMinimax(Consts.minimaxType["maximize"])
:param minimax: the minimax type
"""
self.minimax = minimax
def __repr__(self):
""" Returns the string representation of the population """
ret = "- GPopulation\n"
ret += "\tPopulation Size:\t %d\n" % (self.popSize,)
ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[Consts.sortType.values().index(self.sortType)].capitalize(),)
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
for slot in self.allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
ret+= self.stats.__repr__()
return ret
def __len__(self):
""" Return the length of population """
return len(self.internalPop)
def __getitem__(self, key):
""" Returns the specified individual from population """
return self.internalPop[key]
def __iter__(self):
""" Returns the iterator of the population """
return iter(self.internalPop)
def __setitem__(self, key, value):
""" Set an individual of population """
self.internalPop[key] = value
self.__clear_flags()
def __clear_flags(self):
self.sorted = False
self.statted = False
def getStatistics(self):
""" Return a Statistics class for statistics
:rtype: the :class:`Statistics.Statistics` instance
"""
self.statistics()
return self.stats
def statistics(self):
""" Do statistical analysis of population and set 'statted' to True """
if self.statted: return
logging.debug("Running statistical calc.")
raw_sum = 0
len_pop = len(self)
for ind in xrange(len_pop):
raw_sum += self[ind].score
self.stats["rawMax"] = max(self, key=key_raw_score).score
self.stats["rawMin"] = min(self, key=key_raw_score).score
self.stats["rawAve"] = raw_sum / float(len_pop)
tmpvar = 0.0;
for ind in xrange(len_pop):
s = self[ind].score - self.stats["rawAve"]
s*= s
tmpvar += s
tmpvar/= float((len(self) - 1))
self.stats["rawDev"] = math_sqrt(tmpvar)
self.stats["rawVar"] = tmpvar
self.statted = True
def bestFitness(self, index=0):
""" Return the best scaled fitness individual of population
:param index: the *index* best individual
:rtype: the individual
"""
self.sort()
return self.internalPop[index]
def bestRaw(self):
""" Return the best raw score individual of population
:rtype: the individual
"""
if self.minimax == Consts.minimaxType["minimize"]:
return min(self, key=key_raw_score)
else:
return max(self, key=key_raw_score)
def sort(self):
""" Sort the population """
if self.sorted: return
rev = (self.minimax == Consts.minimaxType["maximize"])
if self.sortType == Consts.sortType["raw"]:
self.internalPop.sort(cmp=cmp_individual_raw, reverse=rev)
else:
self.scale()
self.internalPop.sort(cmp=cmp_individual_scaled, reverse=rev)
self.sorted = True
def setPopulationSize(self, size):
""" Set the population size
:param size: the population size
"""
self.popSize = size
def setSortType(self, sort_type):
""" Sets the sort type
Example:
>>> pop.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
self.sortType = sort_type
def create(self, **args):
""" Clone the example genome to fill the population """
self.clear()
self.minimax = args["minimax"]
for i in xrange(self.popSize):
self.internalPop.append(self.oneSelfGenome.clone())
self.__clear_flags()
def initialize(self):
""" Initialize all individuals of population,
this calls the initialize() of individuals """
for gen in self.internalPop:
gen.initialize()
self.__clear_flags()
def evaluate(self, **args):
""" Evaluate all individuals in population, calls the evaluate() method of individuals
:param args: this params are passed to the evaluation function
"""
for ind in self.internalPop:
ind.evaluate(**args)
self.__clear_flags()
def scale(self, **args):
""" Scale the population using the scaling method
:param args: this parameter is passed to the scale method
"""
for it in self.scaleMethod.applyFunctions(self, **args):
pass
fit_sum = 0
for ind in xrange(len(self)):
fit_sum += self[ind].fitness
self.stats["fitMax"] = max(self, key=key_fitness_score).fitness
self.stats["fitMin"] = min(self, key=key_fitness_score).fitness
self.stats["fitAve"] = fit_sum / float(len(self))
self.sorted = False
def printStats(self):
""" Print statistics of the current population """
message = ""
if self.sortType == Consts.sortType["scaled"]:
message = "Max/Min/Avg Fitness(Raw) [%.2f(%.2f)/%.2f(%.2f)/%.2f(%.2f)]" % (self.stats["fitMax"], self.stats["rawMax"], self.stats["fitMin"], self.stats["rawMin"], self.stats["fitAve"], self.stats["rawAve"])
else:
message = "Max/Min/Avg Raw [%.2f/%.2f/%.2f]" % (self.stats["rawMax"], self.stats["rawMin"], self.stats["rawAve"])
logging.info(message)
print message
return message
def copy(self, pop):
""" Copy current population to 'pop'
:param pop: the destination population
.. warning:: this method do not copy the individuals, only the population logic
"""
pop.popSize = self.popSize
pop.sortType = self.sortType
pop.sorted = self.sorted
pop.statted = self.statted
pop.minimax = self.minimax
pop.scaleMethod = self.scaleMethod
def clear(self):
""" Remove all individuals from population """
del self.internalPop[:]
self.__clear_flags()
def clone(self):
""" Return a brand-new cloned population """
newpop = GPopulation(self.oneSelfGenome.clone())
self.copy(newpop)
return newpop
class MigrationScheme:
""" This is the base class for all migration schemes
:param host: the source hostname
:param port: the source host port
:param group_name: the group name
"""
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
migration_scheme.selector.set(Selectors.GRouletteWheel) """
def __init__(self, host, port, group_name):
self.myself = None
self.groupName = group_name
self.selector = FunctionSlot("Selector")
self.setMyself(host, port)
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.networkCompression = 9
def isReady(self):
""" Returns true if is time to migrate """
return True if self.GAEngine.getCurrentGeneration() % self.nMigrationRate == 0 else False
def getCompressionLevel(self):
""" Get the zlib compression level of network data
The values are in the interval described on the :func:`Network.pickleAndCompress`
"""
return self.networkCompression
def setCompressionLevel(self, level):
""" Set the zlib compression level of network data
The values are in the interval described on the :func:`Network.pickleAndCompress`
:param level: the zlib compression level
"""
self.networkCompression = level
def getNumReplacement(self):
""" Return the number of individuals that will be
replaced in the migration process """
return self.nReplacement
def setNumReplacement(self, num_individuals):
""" Return the number of individuals that will be
replaced in the migration process
:param num_individuals: the number of individuals to be replaced
"""
self.nReplacement = num_individuals
def getNumIndividuals(self):
""" Return the number of individuals that will migrate
:rtype: the number of individuals to be replaced
"""
return self.nIndividuals
def setNumIndividuals(self, num_individuals):
""" Set the number of individuals that will migrate
:param num_individuals: the number of individuals
"""
self.nIndividuals = num_individuals
def setMigrationRate(self, generations):
""" Sets the generation frequency supposed to migrate
and receive individuals.
:param generations: the number of generations
"""
self.nMigrationRate = generations
def getMigrationRate(self):
""" Return the the generation frequency supposed to migrate
and receive individuals
:rtype: the number of generations
"""
return self.nMigrationRate
def setGAEngine(self, ga_engine):
""" Sets the GA Engine handler """
self.GAEngine = ga_engine
def start(self):
""" Initializes the migration scheme """
pass
def stop(self):
""" Stops the migration engine """
pass
def getGroupName(self):
""" Gets the group name
.. note:: all islands of evolution which are supposed to exchange
individuals, must have the same group name.
"""
return self.groupName
def setGroupName(self, name):
""" Sets the group name
:param name: the group name
.. note:: all islands of evolution which are supposed to exchange
individuals, must have the same group name.
"""
self.groupName = name
def setMyself(self, host, port):
""" Which interface you will use to send/receive data
:param host: your hostname
:param port: your port
"""
self.myself = (host, port)
def select(self):
""" Pickes an individual from population using specific selection method
:rtype: an individual object
"""
if self.selector.isEmpty():
return self.GAEngine.select(popID=self.GAEngine.currentGeneration)
else:
for it in self.selector.applyFunctions(self.GAEngine.internalPop, popID=self.GAEngine.currentGeneration):
return it
def selectPool(self, num_individuals):
""" Select num_individuals number of individuals and return a pool
:param num_individuals: the number of individuals to select
:rtype: list with individuals
"""
pool = [self.select() for i in xrange(num_individuals)]
return pool
def exchange(self):
""" Exchange individuals """
pass
class GSimpleGA:
""" GA Engine Class - The Genetic Algorithm Core
Example:
>>> ga = GSimpleGA.GSimpleGA(genome)
>>> ga.selector.set(Selectors.GRouletteWheel)
>>> ga.setGenerations(120)
>>> ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
:param genome: the :term:`Sample Genome`
:param interactiveMode: this flag enables the Interactive Mode, the default is True
:param seed: the random seed value
.. note:: if you use the same random seed, all the runs of algorithm will be the same
"""
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
ga_engine.selector.set(Selectors.GRouletteWheel) """
stepCallback = None
""" This is the :term:`step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(ga_engine):
# Here you have access to the GA Engine
return False
ga_engine.stepCallback.set(your_func)
now *"your_func"* will be called every generation.
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you must do this: ::
ga_engine.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
Now, when you run your GA, it will stop when the population converges.
There are those termination criteria functions: :func:`GSimpleGA.RawScoreCriteria`, :func:`GSimpleGA.ConvergenceCriteria`, :func:`GSimpleGA.RawStatsCriteria`, :func:`GSimpleGA.FitnessStatsCriteria`
But you can create your own termination function, this function receives
one parameter which is the GA Engine, follows an example: ::
def ConvergenceCriteria(ga_engine):
pop = ga_engine.getPopulation()
return pop[0] == pop[len(pop)-1]
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues, this function is called every
generation.
"""
def __init__(self, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
Util.raiseException("The genome must be a GenomeBase subclass",
TypeError)
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [
self.selector, self.stepCallback, self.terminationCriteria
]
self.internalParams = {}
self.currentGeneration = 0
# GP Testing
for classes in Consts.CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d",
self.nGenerations)
def setGPMode(self, bool_value):
""" Sets the Genetic Programming mode of the GA Engine
:param bool_value: True or False
"""
self.GPMode = bool_value
def getGPMode(self):
""" Get the Genetic Programming mode of the GA Engine
:rtype: True or False
"""
return self.GPMode
def __call__(self, *args, **kwargs):
""" A method to implement a callable object
Example:
>>> ga_engine(freq_stats=10)
.. versionadded:: 0.6
The callable method.
"""
if kwargs.get("freq_stats", None):
return self.evolve(kwargs.get("freq_stats"))
else:
return self.evolve()
def setParams(self, **args):
""" Set the internal params
Example:
>>> ga.setParams(gp_terminals=['x', 'y'])
:param args: params to save
..versionaddd:: 0.6
Added the *setParams* method.
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> ga.getParam("gp_terminals")
['x', 'y']
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
..versionaddd:: 0.6
Added the *getParam* method.
"""
return self.internalParams.get(key, nvl)
def setInteractiveGeneration(self, generation):
""" Sets the generation in which the GA must enter in the
Interactive Mode
:param generation: the generation number, use "-1" to disable
.. versionadded::0.6
The *setInteractiveGeneration* method.
"""
if generation < -1:
Util.raiseException("Generation must be >= -1", ValueError)
self.interactiveGen = generation
def getInteractiveGeneration(self):
""" returns the generation in which the GA must enter in the
Interactive Mode
:rtype: the generation number or -1 if not set
.. versionadded::0.6
The *getInteractiveGeneration* method.
"""
return self.interactiveGen
def setElitismReplacement(self, numreplace):
""" Set the number of best individuals to copy to the next generation on the elitism
:param numreplace: the number of individuals
.. versionadded:: 0.6
The *setElitismReplacement* method.
"""
if numreplace < 1:
Util.raiseException("Replacement number must be >= 1", ValueError)
self.nElitismReplacement = numreplace
def setInteractiveMode(self, flag=True):
""" Enable/disable the interactive mode
:param flag: True or False
.. versionadded: 0.6
The *setInteractiveMode* method.
"""
if type(flag) != BooleanType:
Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
self.interactiveMode = flag
def __repr__(self):
""" The string representation of the GA Engine """
ret = "- GSimpleGA\n"
ret += "\tGP Mode:\t\t %s\n" % self.getGPMode()
ret += "\tPopulation Size:\t %d\n" % (self.internalPop.popSize, )
ret += "\tGenerations:\t\t %d\n" % (self.nGenerations, )
ret += "\tCurrent Generation:\t %d\n" % (self.currentGeneration, )
ret += "\tMutation Rate:\t\t %.2f\n" % (self.pMutation, )
ret += "\tCrossover Rate:\t\t %.2f\n" % (self.pCrossover, )
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[
Consts.minimaxType.values().index(self.minimax)].capitalize(), )
ret += "\tElitism:\t\t %s\n" % (self.elitism, )
ret += "\tElitism Replacement:\t %d\n" % (self.nElitismReplacement, )
ret += "\tDB Adapter:\t\t %s\n" % (self.dbAdapter, )
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
return ret
def setMultiProcessing(self,
flag=True,
full_copy=False,
number_of_processes=-1):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
Pyevolve will automaticly check if your Python version has **multiprocessing**
support and if you have more than one single CPU core. If you don't have support
or have just only one core, Pyevolve will not use the **multiprocessing**
feature.
Pyevolve uses the **multiprocessing** to execute the evaluation function over
the individuals, so the use of this feature will make sense if you have a
truly slow evaluation function (which is commom in GAs).
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
:param number_of_processes: None = use the default, or specify the number
.. warning:: Use this option only when your evaluation function is slow, so you'll
get a good tradeoff between the process communication speed and the
parallel evaluation. The use of the **multiprocessing** doesn't means
always a better performance.
.. note:: To enable the multiprocessing option, you **MUST** add the *__main__* check
on your application, otherwise, it will result in errors. See more on the
`Python Docs <http://docs.python.org/library/multiprocessing.html#multiprocessing-programming>`__
site.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
if type(flag) != BooleanType:
Util.raiseException("Multiprocessing option must be True or False",
TypeError)
if type(full_copy) != BooleanType:
Util.raiseException(
"Multiprocessing 'full_copy' option must be True or False",
TypeError)
self.internalPop.setMultiProcessing(flag, full_copy,
number_of_processes)
def setMigrationAdapter(self, migration_adapter=None):
""" Sets the Migration Adapter
.. versionadded:: 0.6
The `setMigrationAdapter` method.
"""
if (migration_adapter is not None) and (not isinstance(
migration_adapter, MigrationScheme)):
Util.raiseException(
"The Migration Adapter must be a MigrationScheme subclass",
TypeError)
self.migrationAdapter = migration_adapter
if self.migrationAdapter is not None:
self.migrationAdapter.setGAEngine(self)
def setDBAdapter(self, dbadapter=None):
""" Sets the DB Adapter of the GA Engine
:param dbadapter: one of the :mod:`DBAdapters` classes instance
.. warning:: the use the of a DB Adapter can reduce the speed performance of the
Genetic Algorithm.
"""
if (dbadapter
is not None) and (not isinstance(dbadapter, DBBaseAdapter)):
Util.raiseException(
"The DB Adapter must be a DBBaseAdapter subclass", TypeError)
self.dbAdapter = dbadapter
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def setSortType(self, sort_type):
""" Sets the sort type, Consts.sortType["raw"]/Consts.sortType["scaled"]
Example:
>>> ga_engine.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
if sort_type not in Consts.sortType.values():
Util.raiseException("sort type must be a Consts.sortType type",
TypeError)
self.internalPop.sortType = sort_type
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0",
ValueError)
self.pMutation = rate
def setPreMutationRate(self, rate):
""" Sets the pre-mutation rate, > 0.0.
Premutation is done only when no cross-over occurs.
:param rate: the rate, >= 0.0
"""
if (rate < 0.0):
Util.raiseException("Pre-mutation rate must be >= 0.0", ValueError)
self.pPreMutation = rate
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0",
ValueError)
self.pCrossover = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1",
ValueError)
self.nGenerations = num_gens
def getGenerations(self):
""" Return the number of generations to evolve
:rtype: the number of generations
.. versionadded:: 0.6
Added the *getGenerations* method
"""
return self.nGenerations
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: the Consts.minimaxType type
"""
return self.minimax
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize",
TypeError)
self.minimax = mtype
def getCurrentGeneration(self):
""" Gets the current generation
:rtype: the current generation
"""
return self.currentGeneration
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != BooleanType:
Util.raiseException("Elitism option must be True or False",
TypeError)
self.elitism = flag
def getDBAdapter(self):
""" Gets the DB Adapter of the GA Engine
:rtype: a instance from one of the :mod:`DBAdapters` classes
"""
return self.dbAdapter
def bestIndividual(self):
""" Returns the population best individual
:rtype: the best individual
"""
return self.internalPop.bestRaw()
def __gp_catch_functions(self, prefix):
""" Internally used to catch functions with some specific prefix
as non-terminals of the GP core """
import __main__ as mod_main
function_set = {}
main_dict = mod_main.__dict__
for obj, addr in main_dict.items():
if obj[0:len(prefix)] == prefix:
try:
op_len = addr.func_code.co_argcount
except:
continue
function_set[obj] = op_len
if len(function_set) <= 0:
Util.raiseException(
"No function set found using function prefix '%s' !" % prefix,
ValueError)
self.setParams(gp_function_set=function_set)
def initialize(self):
""" Initializes the GA Engine. Create and initialize population """
self.internalPop.create(minimax=self.minimax)
self.internalPop.initialize(ga_engine=self)
logging.debug("The GA Engine was initialized !")
def getPopulation(self):
""" Return the internal population of GA Engine
:rtype: the population (:class:`GPopulation.GPopulation`)
"""
return self.internalPop
def getStatistics(self):
""" Gets the Statistics class instance of current generation
:rtype: the statistics instance (:class:`Statistics.Statistics`)
"""
return self.internalPop.getStatistics()
def clear(self):
""" Petrowski's Clearing Method """
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
logging.debug("Population was cloned.")
size_iterate = len(self.internalPop)
# Odd population size
if size_iterate % 2 != 0: size_iterate -= 1
#Check on the crossover function by picking a random individual - is it empty?
crossover_empty = self.select(
popID=self.currentGeneration).crossover.isEmpty()
for i in xrange(0, size_iterate, 2):
#Ok, we select 2 parents using the selector (RouletteWheel, etc.)
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
#Crossover all of them
for it in genomeMom.crossover.applyFunctions(mom=genomeMom,
dad=genomeDad,
count=2):
(sister, brother) = it
else:
#Filp a coin each time to determine if you should crossover
if not crossover_empty and Util.randomFlipCoin(
self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom,
dad=genomeDad,
count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
#And "pre" mutate them
sister.premutate(pmut=self.pPreMutation, ga_engine=self)
brother.premutate(pmut=self.pPreMutation, ga_engine=self)
#Now each offspring is mutated
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
if len(self.internalPop) % 2 != 0:
#Odd-numbered population
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom,
dad=genomeDad,
count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
#Do the 2 mutations
sister.premutate(pmut=self.pPreMutation, ga_engine=self)
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
#---- Evaluate fitness ------
logging.debug("Evaluating the new created population.")
newPop.evaluate()
#Niching methods- Petrowski's clearing
self.clear()
if self.elitism:
#Avoid too much elitism
if self.nElitismReplacement >= len(self.internalPop):
self.nElitismReplacement = len(self.internalPop) - 1
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
#Replace the n-th worst new ones with the nth best old ones
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score > newPop.bestRaw(
i).score:
newPop[len(newPop) - 1 -
i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score < newPop.bestRaw(
i).score:
newPop[len(newPop) - 1 -
i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.",
self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration >= self.nGenerations)
def printStats(self):
""" Print generation statistics
:rtype: the printed statistics as string
.. versionchanged:: 0.6
The return of *printStats* method.
"""
percent = self.currentGeneration * 100 / float(self.nGenerations)
message = "Gen. %d (%.2f%%):" % (self.currentGeneration, percent)
logging.info(message)
print message,
sys_stdout.flush()
self.internalPop.statistics()
stat_ret = self.internalPop.printStats()
return message + stat_ret
def printTimeElapsed(self):
""" Shows the time elapsed since the begin of evolution """
total_time = time() - self.time_init
print "Total time elapsed: %.3f seconds." % total_time
return total_time
def dumpStatsDB(self):
""" Dumps the current statistics to database adapter """
logging.debug("Dumping stats to the DB Adapter")
self.internalPop.statistics()
self.dbAdapter.insert(self)
#----------------------------------------------------------
def evolve(self, freq_stats=0, skip_initialize=False):
""" Do all the generations until the termination criteria, accepts
the freq_stats (default is 0) to dump statistics at n-generation
Example:
>>> ga_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats generation.
:rtype: returns a tuple:
best: the best individual of the evolution
stopFlagCallback: stopped by the step_callback
stopFlagTerminationCriteria: stopped by reching the criterion
.. versionadded:: 0.6
the return of the best individual
"""
stopFlagCallback = False
stopFlagTerminationCriteria = False
self.time_init = time()
logging.debug(
"Starting the DB Adapter and the Migration Adapter if any")
if self.dbAdapter: self.dbAdapter.open(self)
if self.migrationAdapter: self.migrationAdapter.start()
if self.getGPMode():
gp_function_prefix = self.getParam("gp_function_prefix")
if gp_function_prefix is not None:
self.__gp_catch_functions(gp_function_prefix)
if not skip_initialize:
#Create the population
self.initialize()
#Initial fitness evaluation
self.internalPop.evaluate()
self.internalPop.sort()
logging.debug("Starting loop over evolutionary algorithm.")
try:
while True:
if self.migrationAdapter:
logging.debug("Migration adapter: exchange")
self.migrationAdapter.exchange()
self.internalPop.clearFlags()
self.internalPop.sort()
#The step callback is called before each step
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats:
if (self.currentGeneration % freq_stats
== 0) or (self.getCurrentGeneration() == 0):
self.printStats()
if self.dbAdapter:
if self.currentGeneration % self.dbAdapter.getStatsGenFreq(
) == 0:
self.dumpStatsDB()
if stopFlagTerminationCriteria:
logging.debug(
"Evolution stopped by the Termination Criteria !")
if freq_stats:
print "\n\tEvolution stopped by Termination Criteria function !\n"
break
if stopFlagCallback:
logging.debug(
"Evolution stopped by Step Callback function !")
if freq_stats:
print "\n\tEvolution stopped by Step Callback function !\n"
break
#(Here was interactive mode code, removed)
if self.step():
break #exit if the number of generations is equal to the max. number of gens.
#(end While True)
except KeyboardInterrupt:
logging.debug("CTRL-C detected, finishing evolution.")
if freq_stats:
print "\n\tA break was detected, you have interrupted the evolution !\n"
#Finished. Clean up the multiprocessing pool
self.getPopulation().cleanupMultiProcessing()
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
if self.dbAdapter:
logging.debug("Closing the DB Adapter")
if not (self.currentGeneration % self.dbAdapter.getStatsGenFreq()
== 0):
self.dumpStatsDB()
self.dbAdapter.commitAndClose()
if self.migrationAdapter:
logging.debug("Closing the Migration Adapter")
if freq_stats: print "Stopping the migration adapter... ",
self.migrationAdapter.stop()
if freq_stats: print "done !"
return (self.bestIndividual(), stopFlagCallback,
stopFlagTerminationCriteria)
def select(self, **args):
""" Select one individual from population
:param args: this parameters will be sent to the selector
"""
for it in self.selector.applyFunctions(self.internalPop, **args):
return it
class GPopulation:
""" GPopulation Class - The container for the population
**Examples**
Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
>>> pop = ga_engine.getPopulation()
Get the best fitness individual
>>> bestIndividual = pop.bestFitness()
Get the best raw individual
>>> bestIndividual = pop.bestRaw()
Get the statistics from the :class:`Statistics.Statistics` instance
>>> stats = pop.getStatistics()
>>> print stats["rawMax"]
10.4
Iterate, get/set individuals
>>> for ind in pop:
>>> print ind
(...)
>>> for i in xrange(len(pop)):
>>> print pop[i]
(...)
>>> pop[10] = newGenome
>>> pop[10].fitness
12.5
:param genome: the :term:`Sample genome`, or a GPopulation object, when cloning.
"""
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
#Cloning a population?
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.internalParams = genome.internalParams
self.multiProcessing = genome.multiProcessing
self.statted = False
self.stats = Statistics()
self.proc_pool = genome.proc_pool
return
logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.proc_pool = None
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.internalParams = {}
self.multiProcessing = (False, False)
# Statistics
self.statted = False
self.stats = Statistics()
#---------------------------------------------------------------------------------
def setMultiProcessing(self, flag=True, full_copy=False, number_of_processes=None):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
:param number_of_processes: None = use the default, or specify the number
.. warning:: Use this option only when your evaluation function is slow, se you
will get a good tradeoff between the process communication speed and the
parallel evaluation.
"""
#Save the parameters
old_settings = self.multiProcessing
self.multiProcessing = (flag, full_copy, number_of_processes)
#Re-initialize if anything changed.
if (old_settings != self.multiProcessing):
self.initializeMultiProcessing()
#---------------------------------------------------------------------------------
def initializeMultiProcessing(self):
"""Initialize the multiprocessing interface. Create the process pool."""
#Close the pool if it exists (we'll be creating a new one)
self.cleanupMultiProcessing()
if self.multiProcessing[0]:
t1 = time.time()
#Create the process pool with the # of processes
num_proc = self.multiProcessing[2]
if num_proc is None:
self.proc_pool = Pool()
elif num_proc > 0:
self.proc_pool = Pool(processes=num_proc)
else:
self.proc_pool = Pool()
print "Multiprocessing initialized in %03.3f sec; will use %d processors." % ( (time.time()-t1), num_proc )
#---------------------------------------------------------------------------------
def cleanupMultiProcessing(self):
"""Clean up process pools."""
if not self.proc_pool is None:
self.proc_pool.close()
def setMinimax(self, minimax):
""" Sets the population minimax
Example:
>>> pop.setMinimax(Consts.minimaxType["maximize"])
:param minimax: the minimax type
"""
self.minimax = minimax
def __repr__(self):
""" Returns the string representation of the population """
ret = "- GPopulation\n"
ret += "\tPopulation Size:\t %d\n" % (self.popSize,)
ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[Consts.sortType.values().index(self.sortType)].capitalize(),)
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
for slot in self.allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
ret+= self.stats.__repr__()
return ret
def __len__(self):
""" Return the length of population """
return len(self.internalPop)
def __getitem__(self, key):
""" Returns the specified individual from population """
return self.internalPop[key]
def __iter__(self):
""" Returns the iterator of the population """
return iter(self.internalPop)
def __setitem__(self, key, value):
""" Set an individual of population """
self.internalPop[key] = value
self.clearFlags()
def clearFlags(self):
""" Clear the sorted and statted internal flags """
self.sorted = False
self.statted = False
def getStatistics(self):
""" Return a Statistics class for statistics
:rtype: the :class:`Statistics.Statistics` instance
"""
self.statistics()
return self.stats
def statistics(self):
""" Do statistical analysis of population and set 'statted' to True """
if self.statted: return
logging.debug("Running statistical calculations")
raw_sum = 0
fit_sum = 0
len_pop = len(self)
for ind in xrange(len_pop):
raw_sum += self[ind].score
#fit_sum += self[ind].fitness
self.stats["rawMax"] = max(self, key=key_raw_score).score
self.stats["rawMin"] = min(self, key=key_raw_score).score
self.stats["rawAve"] = raw_sum / float(len_pop)
#self.stats["rawTot"] = raw_sum
#self.stats["fitTot"] = fit_sum
tmpvar = 0.0
for ind in xrange(len_pop):
s = self[ind].score - self.stats["rawAve"]
s*= s
tmpvar += s
tmpvar/= float((len(self) - 1))
try:
self.stats["rawDev"] = math_sqrt(tmpvar)
except:
self.stats["rawDev"] = 0.0
self.stats["rawVar"] = tmpvar
self.statted = True
def bestFitness(self, index=0):
""" Return the best scaled fitness individual of population
:param index: the *index* best individual
:rtype: the individual
"""
self.sort()
return self.internalPop[index]
def bestRaw(self, index=0):
""" Return the best raw score individual of population
:param index: the *index* best raw individual
:rtype: the individual
.. versionadded:: 0.6
The parameter `index`.
"""
if self.sortType == Consts.sortType["raw"]:
return self.internalPop[index]
else:
self.sort()
return self.internalPopRaw[index]
def sort(self):
""" Sort the population """
if self.sorted: return
rev = (self.minimax == Consts.minimaxType["maximize"])
if self.sortType == Consts.sortType["raw"]:
self.internalPop.sort(cmp=Util.cmp_individual_raw, reverse=rev)
else:
self.scale()
self.internalPop.sort(cmp=Util.cmp_individual_scaled, reverse=rev)
self.internalPopRaw = self.internalPop[:]
self.internalPopRaw.sort(cmp=Util.cmp_individual_raw, reverse=rev)
self.sorted = True
def setPopulationSize(self, size):
""" Set the population size
:param size: the population size
"""
self.popSize = size
def setSortType(self, sort_type):
""" Sets the sort type
Example:
>>> pop.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
self.sortType = sort_type
def create(self, **args):
""" Clone the example genome to fill the population """
self.minimax = args["minimax"]
self.internalPop = [self.oneSelfGenome.clone() for i in xrange(self.popSize)]
self.clearFlags()
def __findIndividual(self, individual, end):
for i in xrange(end):
if individual.compare(self.internalPop[i]) == 0:
return True
def initialize(self, **args):
""" Initialize all individuals of population,
this calls the initialize() of individuals """
logging.debug("Initializing the population")
if self.oneSelfGenome.getParam("full_diversity", True) and hasattr(self.oneSelfGenome, "compare"):
for i in xrange(len(self.internalPop)):
curr = self.internalPop[i]
curr.initialize(**args)
while self.__findIndividual(curr, i):
curr.initialize(**args)
else:
for gen in self.internalPop:
gen.initialize(**args)
self.clearFlags()
def evaluate(self, **args):
""" Evaluate all individuals in population, calls the evaluate() method of individuals
:param args: this params are passed to the evaluation function
"""
# We have multiprocessing
if self.multiProcessing[0] and MULTI_PROCESSING:
logging.debug("Evaluating the population using the multiprocessing method")
#Make sure we have a process pool.
if self.proc_pool is None:
self.initializeMultiProcessing()
# Multiprocessing full_copy parameter
if self.multiProcessing[1]:
results = self.proc_pool.map(multiprocessing_eval_full, self.internalPop)
for i in xrange(len(self.internalPop)):
self.internalPop[i] = results[i]
else:
results = self.proc_pool.map(multiprocessing_eval, self.internalPop)
for individual, score in zip(self.internalPop, results):
individual.score = score
else:
#Direct evaluation (no multiprocessing)
for ind in self.internalPop:
ind.evaluate(**args)
self.clearFlags()
def scale(self, **args):
""" Scale the population using the scaling method
:param args: this parameter is passed to the scale method
"""
for it in self.scaleMethod.applyFunctions(self, **args):
pass
fit_sum = 0
for ind in xrange(len(self)):
fit_sum += self[ind].fitness
self.stats["fitMax"] = max(self, key=key_fitness_score).fitness
self.stats["fitMin"] = min(self, key=key_fitness_score).fitness
self.stats["fitAve"] = fit_sum / float(len(self))
self.sorted = False
def printStats(self):
""" Print statistics of the current population """
message = ""
if self.sortType == Consts.sortType["scaled"]:
message = "Max/Min/Avg Fitness(Raw) [%(fitMax).2f(%(rawMax).2f)/%(fitMin).2f(%(rawMin).2f)/%(fitAve).2f(%(rawAve).2f)]" % self.stats
else:
message = "Max/Min/Avg Raw [%(rawMax).2f/%(rawMin).2f/%(rawAve).2f]" % self.stats
logging.info(message)
print message
return message
def copy(self, pop):
""" Copy current population to 'pop'
:param pop: the destination population
.. warning:: this method do not copy the individuals, only the population logic
"""
pop.popSize = self.popSize
pop.sortType = self.sortType
pop.minimax = self.minimax
pop.scaleMethod = self.scaleMethod
#pop.internalParams = self.internalParams.copy()
pop.internalParams = self.internalParams
pop.multiProcessing = self.multiProcessing
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> population.getParam("tournamentPool")
5
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def setParams(self, **args):
""" Gets an internal parameter
Example:
>>> population.setParams(tournamentPool=5)
:param args: parameters to set
.. versionadded:: 0.6
The `setParams` method.
"""
self.internalParams.update(args)
def clear(self):
""" Remove all individuals from population """
del self.internalPop[:]
del self.internalPopRaw[:]
self.clearFlags()
def clone(self):
""" Return a brand-new cloned population """
newpop = GPopulation(self.oneSelfGenome)
self.copy(newpop)
return newpop
class MigrationScheme(object):
""" This is the base class for all migration schemes """
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
migration_scheme.selector.set(Selectors.GRouletteWheel) """
def __init__(self):
self.selector = FunctionSlot("Selector")
self.GAEngine = None
self.nMigrationRate = Consts.CDefGenMigrationRate
self.nIndividuals = Consts.CDefMigrationNIndividuals
self.nReplacement = Consts.CDefGenMigrationReplacement
self.networkCompression = 9
def isReady(self):
""" Returns true if is time to migrate """
return True if self.GAEngine.getCurrentGeneration(
) % self.nMigrationRate == 0 else False
def getCompressionLevel(self):
""" Get the zlib compression level of network data
The values are in the interval described on the :func:`Network.pickleAndCompress`
"""
return self.networkCompression
def setCompressionLevel(self, level):
""" Set the zlib compression level of network data
The values are in the interval described on the :func:`Network.pickleAndCompress`
:param level: the zlib compression level
"""
self.networkCompression = level
def getNumReplacement(self):
""" Return the number of individuals that will be
replaced in the migration process """
return self.nReplacement
def setNumReplacement(self, num_individuals):
""" Return the number of individuals that will be
replaced in the migration process
:param num_individuals: the number of individuals to be replaced
"""
self.nReplacement = num_individuals
def getNumIndividuals(self):
""" Return the number of individuals that will migrate
:rtype: the number of individuals to be replaced
"""
return self.nIndividuals
def setNumIndividuals(self, num_individuals):
""" Set the number of individuals that will migrate
:param num_individuals: the number of individuals
"""
self.nIndividuals = num_individuals
def setMigrationRate(self, generations):
""" Sets the generation frequency supposed to migrate
and receive individuals.
:param generations: the number of generations
"""
self.nMigrationRate = generations
def getMigrationRate(self):
""" Return the the generation frequency supposed to migrate
and receive individuals
:rtype: the number of generations
"""
return self.nMigrationRate
def setGAEngine(self, ga_engine):
""" Sets the GA Engine handler """
self.GAEngine = ga_engine
def start(self):
""" Initializes the migration scheme """
pass
def stop(self):
""" Stops the migration engine """
pass
def select(self):
""" Pickes an individual from population using specific selection method
:rtype: an individual object
"""
if self.selector.isEmpty():
return self.GAEngine.select(popID=self.GAEngine.currentGeneration)
else:
for it in self.selector.applyFunctions(
self.GAEngine.internalPop,
popID=self.GAEngine.currentGeneration):
return it
def selectPool(self, num_individuals):
""" Select num_individuals number of individuals and return a pool
:param num_individuals: the number of individuals to select
:rtype: list with individuals
"""
pool = [self.select() for i in xrange(num_individuals)]
return pool
def exchange(self):
""" Exchange individuals """
pass
class GenomeBase(object):
""" GenomeBase Class - The base of all chromosome representation """
__slots__ = ["evaluator", "initializator", "mutator", "crossover", "internalParams", "score", "fitness"]
def __init__(self):
"""Genome Constructor"""
self.evaluator = FunctionSlot("Evaluator")
self.initializator = FunctionSlot("Initializator")
self.mutator = FunctionSlot("Mutator")
self.crossover = FunctionSlot("Crossover")
self.internalParams = {}
self.score = 0.0
self.fitness = 0.0
def getRawScore(self):
""" Get the Raw Score of the genome
:rtype: genome raw score
"""
return self.score
def getFitnessScore(self):
""" Get the Fitness Score of the genome
:rtype: genome fitness score
"""
return self.fitness
def __repr__(self):
"""String representation of Genome"""
allSlots = [self.evaluator, self.initializator, self.mutator,
self.crossover]
ret = "- GenomeBase\n"
ret += "\tScore:\t\t\t %.6f\n" % (self.score,)
ret += "\tFitness:\t\t %.6f\n\n" % (self.fitness,)
ret += "\tParams:\t\t %s\n\n" % (self.internalParams,)
for slot in allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
return ret
def setParams(self, **args):
""" Set the internal params
Example:
>>> genome.setParams(rangemin=0, rangemax=100, gauss_mu=0, gauss_sigma=1)
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param args: this params will saved in every chromosome for genetic op. use
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> genome.getParam("rangemax")
100
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def resetStats(self):
""" Clear score and fitness of genome """
self.score = 0.0
self.fitness = 0.0
def evaluate(self, **args):
""" Called to evaluate genome
:param args: this parameters will be passes to the evaluator
"""
self.resetStats()
for it in self.evaluator.applyFunctions(self, **args):
self.score += it
def initialize(self, **args):
""" Called to initialize genome
:param args: this parameters will be passed to the initializator
"""
for it in self.initializator.applyFunctions(self, **args):
pass
def mutate(self, **args):
""" Called to mutate the genome
:param args: this parameters will be passed to the mutator
:rtype: the number of mutations returned by mutation operator
"""
nmuts = 0
for it in self.mutator.applyFunctions(self, **args):
nmuts += it
return nmuts
def copy(self, g):
""" Copy the current GenomeBase to 'g'
:param g: the destination genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
g.score = self.score
g.fitness = self.fitness
g.evaluator = self.evaluator
g.initializator = self.initializator
g.mutator = self.mutator
g.crossover = self.crossover
g.internalParams = self.internalParams
def clone(self):
""" Clone this GenomeBase
:rtype: the clone genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
newcopy = GenomeBase()
self.copy(newcopy)
return newcopy
class GSimpleGA:
""" GA Engine Class - The Genetic Algorithm Core
Example:
>>> ga = GSimpleGA.GSimpleGA(genome)
>>> ga.selector.set(Selectors.GRouletteWheel)
>>> ga.setGenerations(120)
>>> ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
:param genome: the :term:`Sample Genome`
:param interactiveMode: this flag enables the Interactive Mode, the default is True
:param seed: the random seed value
.. note:: if you use the same random seed, all the runs of algorithm will be the same
"""
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
ga_engine.selector.set(Selectors.GRouletteWheel) """
stepCallback = None
""" This is the :term:`step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(ga_engine):
# Here you have access to the GA Engine
return False
ga_engine.stepCallback.set(your_func)
now *"your_func"* will be called every generation.
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you must do this: ::
ga_engine.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
Now, when you run your GA, it will stop when the population converges.
There are those termination criteria functions: :func:`GSimpleGA.RawScoreCriteria`, :func:`GSimpleGA.ConvergenceCriteria`, :func:`GSimpleGA.RawStatsCriteria`, :func:`GSimpleGA.FitnessStatsCriteria`
But you can create your own termination function, this function receives
one parameter which is the GA Engine, follows an example: ::
def ConvergenceCriteria(ga_engine):
pop = ga_engine.getPopulation()
return pop[0] == pop[len(pop)-1]
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues, this function is called every
generation.
"""
def __init__(self, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
gp.Util.raiseException("Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
gp.Util.raiseException("The genome must be a GenomeBase subclass", TypeError)
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [ self.selector, self.stepCallback, self.terminationCriteria ]
self.internalParams = {}
self.currentGeneration = 0
# gp Testing
CDefGPGenomes = [GTreeGP]
for classes in CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d", self.nGenerations)
def setGPMode(self, bool_value):
""" Sets the Genetic Programming mode of the GA Engine
:param bool_value: True or False
"""
self.GPMode = bool_value
def getGPMode(self):
""" Get the Genetic Programming mode of the GA Engine
:rtype: True or False
"""
return self.GPMode
def __call__(self, *args, **kwargs):
""" A method to implement a callable object
Example:
>>> ga_engine(freq_stats=10)
.. versionadded:: 0.6
The callable method.
"""
if kwargs.get("freq_stats", None):
return self.evolve(kwargs.get("freq_stats"))
else:
return self.evolve()
def setParams(self, **args):
""" Set the internal params
Example:
>>> ga.setParams(gp_terminals=['x', 'y'])
:param args: params to save
..versionaddd:: 0.6
Added the *setParams* method.
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> ga.getParam("gp_terminals")
['x', 'y']
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
..versionaddd:: 0.6
Added the *getParam* method.
"""
return self.internalParams.get(key, nvl)
def setInteractiveGeneration(self, generation):
""" Sets the generation in which the GA must enter in the
Interactive Mode
:param generation: the generation number, use "-1" to disable
.. versionadded::0.6
The *setInteractiveGeneration* method.
"""
if generation < -1:
Util.raiseException("Generation must be >= -1", ValueError)
self.interactiveGen = generation
def getInteractiveGeneration(self):
""" returns the generation in which the GA must enter in the
Interactive Mode
:rtype: the generation number or -1 if not set
.. versionadded::0.6
The *getInteractiveGeneration* method.
"""
return self.interactiveGen
def setElitismReplacement(self, numreplace):
""" Set the number of best individuals to copy to the next generation on the elitism
:param numreplace: the number of individuals
.. versionadded:: 0.6
The *setElitismReplacement* method.
"""
if numreplace < 1:
Util.raiseException("Replacement number must be >= 1", ValueError)
self.nElitismReplacement = numreplace
def setInteractiveMode(self, flag=True):
""" Enable/disable the interactive mode
:param flag: True or False
.. versionadded: 0.6
The *setInteractiveMode* method.
"""
if type(flag) != BooleanType:
Util.raiseException("Interactive Mode option must be True or False", TypeError)
self.interactiveMode = flag
def __repr__(self):
""" The string representation of the GA Engine """
ret = "- GSimpleGA\n"
ret += "\tgp Mode:\t\t %s\n" % self.getGPMode()
ret += "\tPopulation Size:\t %d\n" % (self.internalPop.popSize,)
ret += "\tGenerations:\t\t %d\n" % (self.nGenerations,)
ret += "\tCurrent Generation:\t %d\n" % (self.currentGeneration,)
ret += "\tMutation Rate:\t\t %.2f\n" % (self.pMutation,)
ret += "\tCrossover Rate:\t\t %.2f\n" % (self.pCrossover,)
ret += "\tMinimax Type:\t\t %s\n" % (
Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
ret += "\tElitism:\t\t %s\n" % (self.elitism,)
ret += "\tElitism Replacement:\t %d\n" % (self.nElitismReplacement,)
ret += "\tDB Adapter:\t\t %s\n" % (self.dbAdapter,)
for slot in self.allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
return ret
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def setSortType(self, sort_type):
""" Sets the sort type, Consts.sortType["raw"]/Consts.sortType["scaled"]
Example:
>>> ga_engine.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
if sort_type not in Consts.sortType.values():
Util.raiseException("sort type must be a Consts.sortType type", TypeError)
self.internalPop.sortType = sort_type
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate>1.0) or (rate<0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0", ValueError)
self.pMutation = rate
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate>1.0) or (rate<0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0", ValueError)
self.pCrossover = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1", ValueError)
self.nGenerations = num_gens
def getGenerations(self):
""" Return the number of generations to evolve
:rtype: the number of generations
.. versionadded:: 0.6
Added the *getGenerations* method
"""
return self.nGenerations
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: the Consts.minimaxType type
"""
return self.minimax
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize", TypeError)
self.minimax = mtype
def getCurrentGeneration(self):
""" Gets the current generation
:rtype: the current generation
"""
return self.currentGeneration
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != BooleanType:
Util.raiseException("Elitism option must be True or False", TypeError)
self.elitism = flag
def bestIndividual(self):
""" Returns the population best individual
:rtype: the best individual
"""
return self.internalPop.bestRaw()
def __gp_catch_functions(self, prefix):
""" Internally used to catch functions with some specific prefix
as non-terminals of the gp core """
import __main__ as mod_main
function_set = {}
main_dict = mod_main.__dict__
for obj, addr in main_dict.items():
if obj[0:len(prefix)] == prefix:
try:
op_len = addr.func_code.co_argcount
except:
continue
function_set[obj] = op_len
if len(function_set) <= 0:
Util.raiseException("No function set found using function prefix '%s' !" % prefix, ValueError)
self.setParams(gp_function_set=function_set)
def initialize(self):
""" Initializes the GA Engine. Create and initialize population """
self.internalPop.create(minimax=self.minimax)
self.internalPop.initialize(ga_engine=self)
logging.debug("The GA Engine was initialized !")
def getPopulation(self):
""" Return the internal population of GA Engine
:rtype: the population (:class:`GPopulation.GPopulation`)
"""
return self.internalPop
def getStatistics(self):
""" Gets the Statistics class instance of current generation
:rtype: the statistics instance (:class:`Statistics.Statistics`)
"""
return self.internalPop.getStatistics()
def clear(self):
""" Petrowski's Clearing Method """
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
logging.debug("Population was cloned.")
size_iterate = len(self.internalPop)
# Odd population size
if size_iterate % 2 != 0: size_iterate -= 1
crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty()
from utils import delog
#############
delog.decache("mutate and check...")
#################
for i in xrange(0, size_iterate, 2):
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
delog.deprint_string("over.")
if len(self.internalPop) % 2 != 0:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
logging.debug("Evaluating the new created population.")
newPop.evaluate()
newPop.sort()
#Niching methods- Petrowski's clearing
self.clear()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
# in ecoc, max value is expected.
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
# check duplicate to avoid repeat indivadual
duplicate = False
for j in xrange(len(newPop)-self.nElitismReplacement, len(newPop)):
if self.internalPop.bestRaw(i).score == newPop.bestRaw(j).score:
duplicate = True
break
if duplicate: continue
newPop[len(newPop)-1-i] = newPop[i]
newPop[i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)
def printStats(self):
""" Print generation statistics
:rtype: the printed statistics as string
.. versionchanged:: 0.6
The return of *printStats* method.
"""
percent = self.currentGeneration * 100 / float(self.nGenerations)
#message = "Gen. %d (%.2f%%):" % (self.currentGeneration, percent)
message = "Gen. %3d: " % (self.currentGeneration)
logging.info(message)
print message,
sys_stdout.flush()
self.internalPop.statistics()
stat_ret = self.internalPop.printStats()
return message + stat_ret
def printTimeElapsed(self):
""" Shows the time elapsed since the begin of evolution """
total_time = time()-self.time_init
print "Total time elapsed: %.3f seconds." % total_time
return total_time
def evolve(self, freq_stats=0):
""" Do all the generations until the termination criteria, accepts
the freq_stats (default is 0) to dump statistics at n-generation
Example:
>>> ga_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats generation.
:rtype: returns the best individual of the evolution
.. versionadded:: 0.6
the return of the best individual
"""
stopFlagCallback = False
stopFlagTerminationCriteria = False
self.time_init = time()
if self.getGPMode():
gp_function_prefix = self.getParam("gp_function_prefix")
if gp_function_prefix is not None:
self.__gp_catch_functions(gp_function_prefix)
self.initialize()
logging.debug("Starting loop over evolutionary algorithm.")
try:
while True:
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats:
if (self.currentGeneration % freq_stats == 0) or (self.getCurrentGeneration() == 0):
self.printStats()
if stopFlagTerminationCriteria:
logging.debug("Evolution stopped by the Termination Criteria !")
if freq_stats:
print "\n\tEvolution stopped by Termination Criteria function !\n"
break
if stopFlagCallback:
logging.debug("Evolution stopped by Step Callback function !")
if freq_stats:
print "\n\tEvolution stopped by Step Callback function !\n"
break
if self.interactiveMode:
if sys_platform[:3] == "win":
if msvcrt.kbhit():
if ord(msvcrt.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive Mode...",
logging.debug("Windows Interactive Mode key detected ! generation=%d", self.getCurrentGeneration())
from gp import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##\nPress CTRL-Z to quit interactive mode." % (__version__,)
session_locals = { "ga_engine" : self,
"population" : self.getPopulation(),
"pyevolve" : pyevolve,
"it" : Interaction}
print
code.interact(interact_banner, local=session_locals)
if (self.getInteractiveGeneration() >= 0) and (self.getInteractiveGeneration() == self.getCurrentGeneration()):
print "Loading modules for Interactive Mode...",
logging.debug("Manual Interactive Mode key detected ! generation=%d", self.getCurrentGeneration())
from gp import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##" % (__version__,)
session_locals = { "ga_engine" : self,
"population" : self.getPopulation(),
"pyevolve" : pyevolve,
"it" : Interaction}
print
code.interact(interact_banner, local=session_locals)
if self.step(): break #exit if the number of generations is equal to the max. number of gens.
except KeyboardInterrupt:
logging.debug("CTRL-C detected, finishing evolution.")
if freq_stats: print "\n\tA break was detected, you have interrupted the evolution !\n"
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
return self.bestIndividual()
def select(self, **args):
""" Select one individual from population
:param args: this parameters will be sent to the selector
"""
for it in self.selector.applyFunctions(self.internalPop, **args):
return it
class ParticleBase(object):
"""ParticleBase Class - the base of all particle representation """
evaluator = None
""" This is the :term 'evaluator function' slot, you can add a function with
the *set* method: ::
particle.evaluator.set(eval_func)
"""
position_initializator = None
""" This is the position initialization function of the particle, you can change
the default initializator using the function slot: ::
particle.position_initializator.set(Initializator.G1DListInitializatorDimmension)
In this example, the initializator: func:`Initializators.G1DListInitializatorDimmension``
will be used to create the initial position of the particle..
"""
velocity_initializator = None
""" This is the velocity initialization function of the particle, you can change
the default initializator using the function slot: ::
particle.velocity_initializator.set(Initializator.G1DListInitializatorDimmension)
In this example, the initializator: func:`Initializators.G1DListInitializatorDimmension``
will be used to create the initial velocity of the particle..
"""
position_communicator = None
""" This is the position communication function slot, you can change the default
communicator using the slot *set* function: ::
particle.position_communicator.set(Communicators.P1DGlobalPosCommunicator)
"""
information_communicator = None
""" This is the information communication function slot, you can change the default
communicator usingt the slot *set* function: ::
particle.information_communicator.set(Communicators.P1DGlobalInfoCommunicator)
"""
def __init__(self):
""" Particle Constructor """
self.evaluator = FunctionSlot("Evaluator")
self.position_initializator = FunctionSlot("Position Initializator")
self.velocity_initializator = FunctionSlot(" Velocity Initializator")
self.position_communicator = FunctionSlot("Position Communicator")
self.information_communicator = FunctionSlot("Information Communicator")
self.allSlots = [self.evaluator, self.position_initializator,
self.velocity_initializator, self.position_communicator, self.information_communicator]
self.internalParams = {}
self.fitness = 0.0
self.ownBestFitness = 0.0
def getFitness(self):
""" Get the Fitness Score of the particle"
:rtype particle fitness score
"""
return self.fitness
def getOwnBestFitness(self):
"""Get the best Fitness score of the particle
:rtype particle best fitness score
"""
return self.ownBestFitness
def __repr__(self):
""" String representation of the Particle"""
ret = "- ParticleBase\n"
ret += "\tFitness:\t\t\t %.6f\n" %(self.fitness,)
ret += "\tOwnBestFitness:\t\t\t %.6f\n" %(self.ownBestFitness,)
ret += "\tInit Params:\t\t %s\n\n" %(self.internalParams,)
for slot in self.allSlots:
ret += "\t"+ slot.__repr__()
ret += "\n"
return ret
def setOwnBestFitness(self,fitness):
""" Set the best fitness of the particle
:param fitness: the best fitness of the particle
"""
self.ownBestFitness = fitness
def setParams(self, **args):
"""Set the initializator params"
Example:
>>> particle.setParams(rangemin=0, rangeMax=100,dimmensions=4)
:param args: this params will saved in every particle for swarm op. use
"""
self.internalParams.update(args)
def getParam(self,key,nvl=None):
""" Gets an initialization parameter
Example:
>>> particle.getParam("rangemax")
100
:param key: the key of parma
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key,nvl)
def resetStats(self):
"""Clear fitness of the particle """
self.fitness = 0.0
def evaluate(self, **args):
""" Called to evaluate the particle
:param args: these parameters will be passed to the evaluator
"""
self.resetStats()
for it in self.evaluator.applyFunctions(self, **args):
self.fitness += it
def initializePosition(self, **args):
"""Called to initialize the particle position
:param args: these parameters will be passed to the initializator
"""
for it in self.position_initializator.applyFunctions(self, **args):
pass
def initializeVelocity(self, **args):
"""Called to initialize the particle velocity
:param args: these parameters will be passed to the initializator
"""
for it in self.velocity_initializator.applyFunctions(self,**args):
pass
def copy(self, other):
""" Copy the current GenomeBase to 'g'
:param other: the destination particle
"""
other.fitness = self.fitness
other.ownBestFitness = self.ownBestFitness
other.evaluator = self.evaluator
other.position_initializator = self.position_initializator
other.velocity_initializator = self.velocity_initializator
other.position_communicator = self.position_communicator
other.information_communicator = self.information_communicator
other.allSlots = self.allSlots[:]
other.internalParams = self.internalParams.copy()
def clone(self):
""" Clone this ParticleBase
:rtype: the clone particle
"""
newcopy = ParticleBase()
self.copy(newcopy)
return newcopy
class GPopulation:
""" GPopulation Class - The container for the population
**Examples**
Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
>>> pop = ga_engine.getPopulation()
Get the best fitness individual
>>> bestIndividual = pop.bestFitness()
Get the best raw individual
>>> bestIndividual = pop.bestRaw()
Get the statistics from the :class:`Statistics.Statistics` instance
>>> stats = pop.getStatistics()
>>> print stats["rawMax"]
10.4
Iterate, get/set individuals
>>> for ind in pop:
>>> print ind
(...)
>>> for i in xrange(len(pop)):
>>> print pop[i]
(...)
>>> pop[10] = newGenome
>>> pop[10].fitness
12.5
:param genome: the :term:`Sample genome`, or a GPopulation object, when cloning.
"""
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.internalParams = genome.internalParams
self.statted = False
self.stats = Statistics()
return
logging.debug("New population instance, %s class genomes.",
genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.internalParams = {}
# Statistics
self.statted = False
self.stats = Statistics()
def setMinimax(self, minimax):
""" Sets the population minimax
Example:
>>> pop.setMinimax(Consts.minimaxType["maximize"])
:param minimax: the minimax type
"""
self.minimax = minimax
def __repr__(self):
""" Returns the string representation of the population """
ret = "- GPopulation\n"
ret += "\tPopulation Size:\t %d\n" % (self.popSize, )
ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[
Consts.sortType.values().index(self.sortType)].capitalize(), )
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[
Consts.minimaxType.values().index(self.minimax)].capitalize(), )
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
ret += self.stats.__repr__()
return ret
def __len__(self):
""" Return the length of population """
return len(self.internalPop)
def __getitem__(self, key):
""" Returns the specified individual from population """
return self.internalPop[key]
def __iter__(self):
""" Returns the iterator of the population """
return iter(self.internalPop)
def __setitem__(self, key, value):
""" Set an individual of population """
self.internalPop[key] = value
self.clearFlags()
def clearFlags(self):
""" Clear the sorted and statted internal flags """
self.sorted = False
self.statted = False
def getStatistics(self):
""" Return a Statistics class for statistics
:rtype: the :class:`Statistics.Statistics` instance
"""
self.statistics()
return self.stats
def statistics(self):
""" Do statistical analysis of population and set 'statted' to True """
if self.statted: return
logging.debug("Running statistical calculations")
raw_sum = 0
fit_sum = 0
len_pop = len(self)
for ind in xrange(len_pop):
raw_sum += self[ind].score
#fit_sum += self[ind].fitness
self.stats["rawMax"] = max(self, key=key_raw_score).score
self.stats["rawMin"] = min(self, key=key_raw_score).score
self.stats["rawAve"] = raw_sum / float(len_pop)
self.stats["Best-Fscore"] = max(self, key=key_raw_score).fscore
self.stats["Best-Hamdist"] = max(self, key=key_raw_score).hamdist
self.stats["Best-Accuracy"] = max(self, key=key_raw_score).accuracy
#self.stats["rawTot"] = raw_sum
#self.stats["fitTot"] = fit_sum
tmpvar = 0.0
for ind in xrange(len_pop):
s = self[ind].score - self.stats["rawAve"]
s *= s
tmpvar += s
tmpvar /= float((len(self) - 1))
try:
self.stats["rawDev"] = math_sqrt(tmpvar)
except:
self.stats["rawDev"] = 0.0
self.stats["rawVar"] = tmpvar
self.statted = True
def bestFitness(self, index=0):
""" Return the best scaled fitness individual of population
:param index: the *index* best individual
:rtype: the individual
"""
self.sort()
return self.internalPop[index]
def bestRaw(self, index=0):
""" Return the best raw score individual of population
:param index: the *index* best raw individual
:rtype: the individual
.. versionadded:: 0.6
The parameter `index`.
"""
if self.sortType == Consts.sortType["raw"]:
return self.internalPop[index]
else:
self.sort()
return self.internalPopRaw[index]
def sort(self):
""" Sort the population """
if self.sorted: return
rev = (self.minimax == Consts.minimaxType["maximize"])
if self.sortType == Consts.sortType["raw"]:
self.internalPop.sort(cmp=Util.cmp_individual_raw, reverse=rev)
else:
self.scale()
self.internalPop.sort(cmp=Util.cmp_individual_scaled, reverse=rev)
self.internalPopRaw = self.internalPop[:]
self.internalPopRaw.sort(cmp=Util.cmp_individual_raw, reverse=rev)
self.sorted = True
def setPopulationSize(self, size):
""" Set the population size
:param size: the population size
"""
self.popSize = size
def setSortType(self, sort_type):
""" Sets the sort type
Example:
>>> pop.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
self.sortType = sort_type
def create(self, **args):
""" Clone the example genome to fill the population """
self.minimax = args["minimax"]
self.internalPop = [
self.oneSelfGenome.clone() for i in xrange(self.popSize)
]
self.clearFlags()
def __findIndividual(self, individual, end):
for i in xrange(end):
if individual.compare(self.internalPop[i]) == 0:
return True
def initialize(self, **args):
""" Initialize all individuals of population,
this calls the initialize() of individuals """
logging.debug("Initializing the population")
if self.oneSelfGenome.getParam("full_diversity", True) and hasattr(
self.oneSelfGenome, "compare"):
for i in xrange(len(self.internalPop)):
curr = self.internalPop[i]
curr.initialize(**args)
while self.__findIndividual(curr, i):
curr.initialize(**args)
else:
for gen in self.internalPop:
gen.initialize(**args)
self.clearFlags()
def evaluate(self, **args):
""" Evaluate all individuals in population, calls the evaluate() method of individuals
:param args: this params are passed to the evaluation function
"""
delog.decache("evaluate...")
for ind in self.internalPop:
ind.evaluate(**args)
self.clearFlags()
delog.deprint_string("over.")
def scale(self, **args):
""" Scale the population using the scaling method
:param args: this parameter is passed to the scale method
"""
for it in self.scaleMethod.applyFunctions(self, **args):
pass
fit_sum = 0
for ind in xrange(len(self)):
fit_sum += self[ind].fitness
self.stats["fitMax"] = max(self, key=key_fitness_score).fitness
self.stats["fitMin"] = min(self, key=key_fitness_score).fitness
self.stats["fitAve"] = fit_sum / float(len(self))
self.sorted = False
def printStats(self):
""" Print statistics of the current population """
message = ""
if self.sortType == Consts.sortType["scaled"]:
'''
format_str = '%%-8s %%-8s %%-8%s %%-10%s %%-10%s'
message = (format_str % ('s', 's', 's')) % ('Max', 'Min', 'Avg', 'Best-Fscore', 'Best-Hamdist')
message = "Max/Min/Avg Fitness(Raw) [%(fitMax).2f(%(rawMax).2f)/%(fitMin).2f(%(rawMin).2f)/%(fitAve).2f(%(rawAve).2f)]" % self.stats
'''
format_str = '%(rawMax).2f %(rawMin).2f %(rawAve).2f %(Best-Fscore).2f %(Best-Hamdist).2f %(Best-Accuracy).2f'
message = format_str % self.stats
else:
format_str = '%(rawMax).2f %(rawMin).2f %(rawAve).2f %(Best-Fscore).2f %(Best-Hamdist).2f %(Best-Accuracy).2f'
message = format_str % self.stats
# message = "Max/Min/Avg Raw [%(rawMax).2f/%(rawMin).2f/%(rawAve).2f]" % self.stats
logging.info(message)
print message + "\n"
return message
def copy(self, pop):
""" Copy current population to 'pop'
:param pop: the destination population
.. warning:: this method do not copy the individuals, only the population logic
"""
pop.popSize = self.popSize
pop.sortType = self.sortType
pop.minimax = self.minimax
pop.scaleMethod = self.scaleMethod
#pop.internalParams = self.internalParams.copy()
pop.internalParams = self.internalParams
pop.multiProcessing = self.multiProcessing
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> population.getParam("tournamentPool")
5
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def setParams(self, **args):
""" Gets an internal parameter
Example:
>>> population.setParams(tournamentPool=5)
:param args: parameters to set
.. versionadded:: 0.6
The `setParams` method.
"""
self.internalParams.update(args)
def clear(self):
""" Remove all individuals from population """
del self.internalPop[:]
del self.internalPopRaw[:]
self.clearFlags()
def clone(self):
""" Return a brand-new cloned population """
newpop = GPopulation(self.oneSelfGenome)
self.copy(newpop)
return newpop
class SimplePSO(object):
""" SimplePSO Engine Class - The PSO Algorithm Core
Example:
>>> topology = Topology.GlobalTopology(particle_rep)
>>> pso = PSO.SimplePSO(topology)
>>> pso.setSteps(120)
:param topology: the :term:`Sample Topology``
:param interactiveMode: this flag enables the Interactive Mode
:param seed: the random seed value
.. note:: if you see the same random seed, all the runs of the algorithm will be the same.
"""
stepCallBack = None
""" This is the the :term: `step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(pso_engine):
#Here you have access to the PSO Engine
return False
pso_engine.stepCallback.set(your_func)
now *"your_func"* will be called every step.
When this function returns True, the PSO Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you mus do this: ::
pso_engine.terminationCriteria.set(your_func)
Now, when you run your PSO, it will stop when terminationCriteria be satisfied.
To create your own termination function, you must put at least one parameter
which is the PSO Engine, follows an example: ::
def ConvergenceCriteria(pso_engine):
swarm = pso_engine.getSwarm()
return swarm[0] == swarm[len(swarm)-1]
When this function returns True, the Pso Engine will stop the evolution and show
a warning. If is False, the evolution continues, this function is called every
step.
"""
def __init__(self,topology,seed=None,interactiveMode=True):
""" Initializator of PSO """
#random seed
random.seed(seed)
#Pso type used by the particle
self.psoType = Consts.CDefPsoType
#Topology used
self.topology = topology
#Set the population size
self.setSwarmSize(Consts.CDefSwarmSize)
#Cognitive and Social Coefficients
self.C1,self.C2 = Consts.CDefCoefficients
#Time steps
self.timeSteps = Consts.CDefSteps
#Interactive Mode (True or False)
self.interactiveMode = interactiveMode
#Current step
self.currentStep = 0
#Inertia Factor Minus
self.inertiaFactorMinus = None
#Inertia coefficient
self.inertiaFactor = None
#Time initial
self.time_init = None
#Optimization type
self.minimax = Consts.minimaxType["minimize"]
#Report file adapter
self.reportAdapter = None
#Step Callback
self.stepCallback = FunctionSlot("Step Callback")
#Termination Criteria
self.terminationCriteria = FunctionSlot("Termination Criteria")
#All slots
self.allSlots = [self.stepCallback, self.terminationCriteria]
print "A PSO Engine was created, timeSteps=% d" % ( self.timeSteps, )
def __repr__(self):
""" The String representation of the PSO Engine """
ret = "- PSO-%s-%s Execution\n" % (self.getTopologyType(),self.getPsoType())
ret += "\tSwarm Size:\t %d\n" % (self.topology.swarmSize,)
ret += "\tTime Steps:\t %d\n" % (self.timeSteps,)
ret += "\tCurrent Step:\t %d\n" % (self.currentStep,)
ret += "\tMinimax Type:\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
ret += "\tReport Adapter:\t %s\n" % (self.reportAdapter,)
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret +="\n"
return ret
def setReportAdapter(self,repadapter):
""" Sets the Report Adapter of the PSO Engine
:param repadapter: one of the :mod:`ReportAdapters` classes instance
.. warning: the use of a Report Adapter can reduce the speed performance of the PSO.
"""
self.reportAdapter = repadapter
def setSwarmSize(self, size):
""" Sets the swarm size, calls setSwarmSize() of Topology
:param size: the swarm size
.. note:: the swarm size must be >= 2
"""
if size < 2:
Util.raiseException("swarm size must be >= 2", ValueError)
self.topology.setSwarmSize(size)
def setPsoType(self,psoType):
""" Sets the psoType, use Consts.psoType(Basic,Constricted,Inertia)
Example:
>>> pso_engine.setSortType(Consts.psoType["CONSTRICTED"])
:param psoType: The PSO type, from Consts.psoType
"""
if psoType not in Consts.psoType.values():
Util.raiseException("PsoType must be implemented !",TypeError)
self.psoType = psoType
def getPsoType(self):
""" Return the Pso Type
:rtype key: pso Type
"""
for key,value in Consts.psoType.items():
if value == self.psoType:
return key
return ""
def setTimeSteps(self,num_steps):
""" Sets the number of steps to converge
:param num_steps: the number of steps
"""
if num_steps < 1:
Util.raiseException("Number of steps must be >=1", ValueError)
self.timeSteps = num_steps
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: The Consts.minimaxType type
"""
for key,value in Consts.minimaxType.items():
if value == self.minimax:
return key
return ""
def getTopologyType(self):
""" Returns the name of the topology
:rtype name: the name of the topology
"""
return self.topology.__class__.__name__
def setMinimax(self,minimax):
"""Sets the minimize/maximize mode, use Consts.minimaxType
:param minimax: the minimax mode, from Consts.minimaxType
"""
if minimax not in Consts.minimaxType.values():
Util.raiseException("Optimization type must be Maximize or Minimize !", TypeError)
self.minimax = minimax
def getCurrentStep(self):
""" Gets the current step
:rtype: the current step
"""
return self.currentStep
def getReportAdapter(self):
""" Gets the Report Adapter of the PSO Engine
:rtype: a instance from one of the :mod:`ReportAdapters` classes
"""
return self.reportAdapter
def bestParticle(self):
""" Returns the swarm best Particle
:rtype: the best particle
"""
return self.topology.getBestParticle()
def getTopology(self):
"""Return the internal topology of Pso Engine
:rtype: the topology (:class: 'Topology.Topology')'
"""
return self.topology
def getStatistics(self):
""" Gets the Statistics class instance of the current step
:rtype: the statistics instance (:class: `Statistics.Statistics`)`
"""
return self.topology.getStatistics()
def dumpStatsReport(self):
""" Dumps the current statistics to the report adapter """
self.topology.statistics()
self.reportAdapter.insert(self.getStatistics(),self.topology,self.currentStep)
def printStats(self):
""" Print swarm statistics"""
percent = self.currentStep * 100 / float(self.timeSteps)
message = "Step: %d (%.2f%%):" % (self.currentStep, percent)
print message
self.topology.statistics()
self.topology.printStats()
def printTimeElapsed(self):
""" Shows the time elapsed since the beginning of the solution construction """
print "Total time elapsed: %.3f seconds." % (time()-self.time_init)
def initialize(self):
""" Initializes the PSO Engine. Create and initialize the swarm """
self.topology.create(minimax=self.minimax)
self.topology.initialize()
print "The PSO Engine was initialized !"
def constructSolution(self):
""" Just do one step in execution, one step."""
for it in self.topology.position_updater.applyFunctions(self):
pass
for it in self.topology.information_updater.applyFunctions(self):
pass
if self.psoType == Consts.psoType["INERTIA"]:
self.updateInertiaFactor()
self.currentStep += 1
return (self.currentStep == self.timeSteps)
def execute(self, freq_stats=0):
""" Do all the steps until the termination criteria or time Steps achieved,
accepts the freq_stats (default is 0) to dump statistics at n-step
Example:
>>> pso_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats step.
"""
#Start time
self.time_init = time()
#Creates a new report if reportAdapter is not None.
if self.reportAdapter: self.reportAdapter.open()
#Initialize the PSO Engine
self.initialize() #Already evaluates all particles
print "Starting loop over evolutionary algorithm."
try:
while not self.constructSolution():
stopFlagCallback = False
stopFlagTerminationCriteria = False
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats != 0:
if (self.currentStep % freq_stats == 0) or (self.currentStep == 1):
self.printStats()
if self.reportAdapter:
if self.currentStep % self.reportAdapter.statsGenFreq == 0:
self.dumpStatsReport()
if stopFlagTerminationCriteria:
print '\n\tExecution stopped by Termination Criteria function !\n'
break
if stopFlagCallback:
print '\n\tExecution stopped by Step Callback function!\n'
break
if self.interactiveMode:
if sys_platform[:3] == "win":
if msvcrt.kbhit():
if ord(msvcrt.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive mode...",
import pypso.Interaction
print "done!\n"
interact_banner = "## PyPSO v.%s - Interactive Mode ##\nPress CTRL-Z to quit interactive mode." % (pypso.__version__,)
session_locals = { "pso_engine" : self,
"topology" : self.getTopology(),
"swarm_statistics": self.getTopology().swarmStats,
"topology_statistics": self.getTopology().topologyStats,
"pypso" : pypso ,
"it" : pypso.Interaction}
print
code.interact(interact_banner, local=session_locals)
elif sys_platform[:5] == "linux":
if Util.kbhit():
if ord(Util.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive mode...",
import pypso.Interaction
print "done!\n"
interact_banner = "## PyPSO v.%s - Interactive Mode ##\nPress CTRL-D to quit interactive mode." % (pypso.__version__,)
session_locals = { "pso_engine" : self,
"topology" : self.getTopology(),
"swarm_statistics": self.getTopology().swarmStats,
"topology_statistics": self.getTopology().topologyStats,
"pypso" : pypso ,
"it" : pypso.Interaction}
print
code.interact(interact_banner, local=session_locals)
except KeyboardInterrupt:
print "\n\tA break was detected, you have interrupted the evolution !\n"
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
if self.reportAdapter:
if (self.currentStep % self.reportAdapter.statsGenFreq == 0):
self.dumpStatsReport()
self.reportAdapter.saveAndClose()
class GSimpleGA:
""" GA Engine Class - The Genetic Algorithm Core
Example:
>>> ga = GSimpleGA.GSimpleGA(genome)
>>> ga.selector.set(Selectors.GRouletteWheel)
>>> ga.setGenerations(120)
>>> ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
:param genome: the :term:`Sample Genome`
:param interactiveMode: this flag enables the Interactive Mode, the default is True
:param seed: the random seed value
.. note:: if you use the same random seed, all the runs of algorithm will be the same
"""
selector = None
""" This is the function slot for the selection method
if you want to change the default selector, you must do this: ::
ga_engine.selector.set(Selectors.GRouletteWheel) """
stepCallback = None
""" This is the :term:`step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(ga_engine):
# Here you have access to the GA Engine
return False
ga_engine.stepCallback.set(your_func)
now *"your_func"* will be called every generation.
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you must do this: ::
ga_engine.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
Now, when you run your GA, it will stop when the population converges.
There are those termination criteria functions: :func:`GSimpleGA.RawScoreCriteria`, :func:`GSimpleGA.ConvergenceCriteria`, :func:`GSimpleGA.RawStatsCriteria`, :func:`GSimpleGA.FitnessStatsCriteria`
But you can create your own termination function, this function receives
one parameter which is the GA Engine, follows an example: ::
def ConvergenceCriteria(ga_engine):
pop = ga_engine.getPopulation()
return pop[0] == pop[len(pop)-1]
When this function returns True, the GA Engine will stop the evolution and show
a warning, if is False, the evolution continues, this function is called every
generation.
"""
def __init__(self, genome, owner, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
Util.raiseException("The genome must be a GenomeBase subclass",
TypeError)
self.internalPop = GPopulation(genome)
self.nGenerations = Consts.CDefGAGenerations
self.pMutation = Consts.CDefGAMutationRate
self.pCrossover = Consts.CDefGACrossoverRate
self.nElitismReplacement = Consts.CDefGAElitismReplacement
self.setPopulationSize(Consts.CDefGAPopulationSize)
self.minimax = Consts.minimaxType["maximize"]
self.elitism = True
self.owner = owner ## added 12/15 by Peter Graf, so GA can evaluate constraints
## and, now (4/16), so we can save let the owner save the state
# Adapters
self.dbAdapter = None
self.migrationAdapter = None
self.time_init = None
self.interactiveMode = interactiveMode
self.interactiveGen = -1
self.GPMode = False
self.selector = FunctionSlot("Selector")
self.stepCallback = FunctionSlot("Generation Step Callback")
self.terminationCriteria = FunctionSlot("Termination Criteria")
self.selector.set(Consts.CDefGASelector)
self.allSlots = [
self.selector, self.stepCallback, self.terminationCriteria
]
self.internalParams = {}
self.currentGeneration = 0
# GP Testing
for classes in Consts.CDefGPGenomes:
if isinstance(self.internalPop.oneSelfGenome, classes):
self.setGPMode(True)
break
logging.debug("A GA Engine was created, nGenerations=%d",
self.nGenerations)
def setGPMode(self, bool_value):
""" Sets the Genetic Programming mode of the GA Engine
:param bool_value: True or False
"""
self.GPMode = bool_value
def getGPMode(self):
""" Get the Genetic Programming mode of the GA Engine
:rtype: True or False
"""
return self.GPMode
def __call__(self, *args, **kwargs):
""" A method to implement a callable object
Example:
>>> ga_engine(freq_stats=10)
.. versionadded:: 0.6
The callable method.
"""
if kwargs.get("freq_stats", None):
return self.evolve(kwargs.get("freq_stats"))
else:
return self.evolve()
def setParams(self, **args):
""" Set the internal params
Example:
>>> ga.setParams(gp_terminals=['x', 'y'])
:param args: params to save
..versionaddd:: 0.6
Added the *setParams* method.
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> ga.getParam("gp_terminals")
['x', 'y']
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
..versionaddd:: 0.6
Added the *getParam* method.
"""
return self.internalParams.get(key, nvl)
def setInteractiveGeneration(self, generation):
""" Sets the generation in which the GA must enter in the
Interactive Mode
:param generation: the generation number, use "-1" to disable
.. versionadded::0.6
The *setInteractiveGeneration* method.
"""
if generation < -1:
Util.raiseException("Generation must be >= -1", ValueError)
self.interactiveGen = generation
def getInteractiveGeneration(self):
""" returns the generation in which the GA must enter in the
Interactive Mode
:rtype: the generation number or -1 if not set
.. versionadded::0.6
The *getInteractiveGeneration* method.
"""
return self.interactiveGen
def setElitismReplacement(self, numreplace):
""" Set the number of best individuals to copy to the next generation on the elitism
:param numreplace: the number of individuals
.. versionadded:: 0.6
The *setElitismReplacement* method.
"""
if numreplace < 1:
Util.raiseException("Replacement number must be >= 1", ValueError)
self.nElitismReplacement = numreplace
def setInteractiveMode(self, flag=True):
""" Enable/disable the interactive mode
:param flag: True or False
.. versionadded: 0.6
The *setInteractiveMode* method.
"""
if type(flag) != BooleanType:
Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
self.interactiveMode = flag
def __repr__(self):
""" The string representation of the GA Engine """
ret = "- GSimpleGA\n"
ret += "\tGP Mode:\t\t %s\n" % self.getGPMode()
ret += "\tPopulation Size:\t %d\n" % (self.internalPop.popSize, )
ret += "\tGenerations:\t\t %d\n" % (self.nGenerations, )
ret += "\tCurrent Generation:\t %d\n" % (self.currentGeneration, )
ret += "\tMutation Rate:\t\t %.2f\n" % (self.pMutation, )
ret += "\tCrossover Rate:\t\t %.2f\n" % (self.pCrossover, )
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[
Consts.minimaxType.values().index(self.minimax)].capitalize(), )
ret += "\tElitism:\t\t %s\n" % (self.elitism, )
ret += "\tElitism Replacement:\t %d\n" % (self.nElitismReplacement, )
ret += "\tDB Adapter:\t\t %s\n" % (self.dbAdapter, )
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
return ret
def setMultiProcessing(self, flag=True, full_copy=False):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
Pyevolve will automaticly check if your Python version has **multiprocessing**
support and if you have more than one single CPU core. If you don't have support
or have just only one core, Pyevolve will not use the **multiprocessing**
feature.
Pyevolve uses the **multiprocessing** to execute the evaluation function over
the individuals, so the use of this feature will make sense if you have a
truly slow evaluation function (which is commom in GAs).
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
.. warning:: Use this option only when your evaluation function is slow, so you'll
get a good tradeoff between the process communication speed and the
parallel evaluation. The use of the **multiprocessing** doesn't means
always a better performance.
.. note:: To enable the multiprocessing option, you **MUST** add the *__main__* check
on your application, otherwise, it will result in errors. See more on the
`Python Docs <http://docs.python.org/library/multiprocessing.html#multiprocessing-programming>`__
site.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
if type(flag) != BooleanType:
Util.raiseException("Multiprocessing option must be True or False",
TypeError)
if type(full_copy) != BooleanType:
Util.raiseException(
"Multiprocessing 'full_copy' option must be True or False",
TypeError)
self.internalPop.setMultiProcessing(flag, full_copy)
def setMigrationAdapter(self, migration_adapter=None):
""" Sets the Migration Adapter
.. versionadded:: 0.6
The `setMigrationAdapter` method.
"""
self.migrationAdapter = migration_adapter
if self.migrationAdapter is not None:
self.migrationAdapter.setGAEngine(self)
def setDBAdapter(self, dbadapter=None):
""" Sets the DB Adapter of the GA Engine
:param dbadapter: one of the :mod:`DBAdapters` classes instance
.. warning:: the use the of a DB Adapter can reduce the speed performance of the
Genetic Algorithm.
"""
if (dbadapter
is not None) and (not isinstance(dbadapter, DBBaseAdapter)):
Util.raiseException(
"The DB Adapter must be a DBBaseAdapter subclass", TypeError)
self.dbAdapter = dbadapter
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def setSortType(self, sort_type):
""" Sets the sort type, Consts.sortType["raw"]/Consts.sortType["scaled"]
Example:
>>> ga_engine.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
if sort_type not in Consts.sortType.values():
Util.raiseException("sort type must be a Consts.sortType type",
TypeError)
self.internalPop.sortType = sort_type
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0",
ValueError)
self.pMutation = rate
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0",
ValueError)
self.pCrossover = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1",
ValueError)
self.nGenerations = num_gens
def getGenerations(self):
""" Return the number of generations to evolve
:rtype: the number of generations
.. versionadded:: 0.6
Added the *getGenerations* method
"""
return self.nGenerations
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: the Consts.minimaxType type
"""
return self.minimax
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize",
TypeError)
self.minimax = mtype
def getCurrentGeneration(self):
""" Gets the current generation
:rtype: the current generation
"""
return self.currentGeneration
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != BooleanType:
Util.raiseException("Elitism option must be True or False",
TypeError)
self.elitism = flag
def getDBAdapter(self):
""" Gets the DB Adapter of the GA Engine
:rtype: a instance from one of the :mod:`DBAdapters` classes
"""
return self.dbAdapter
def bestIndividual(self):
""" Returns the population best individual
:rtype: the best individual
"""
return self.internalPop.bestRaw()
def __gp_catch_functions(self, prefix):
""" Internally used to catch functions with some specific prefix
as non-terminals of the GP core """
import __main__ as mod_main
function_set = {}
main_dict = mod_main.__dict__
for obj, addr in main_dict.items():
if obj[0:len(prefix)] == prefix:
try:
op_len = addr.func_code.co_argcount
except:
continue
function_set[obj] = op_len
if len(function_set) <= 0:
Util.raiseException(
"No function set found using function prefix '%s' !" % prefix,
ValueError)
self.setParams(gp_function_set=function_set)
def initialize(self):
""" Initializes the GA Engine. Create and initialize population """
self.internalPop.create(minimax=self.minimax)
self.internalPop.initialize(ga_engine=self)
logging.debug("The GA Engine was initialized !")
def getPopulation(self):
""" Return the internal population of GA Engine
:rtype: the population (:class:`GPopulation.GPopulation`)
"""
return self.internalPop
def getStatistics(self):
""" Gets the Statistics class instance of current generation
:rtype: the statistics instance (:class:`Statistics.Statistics`)
"""
return self.internalPop.getStatistics()
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
popsize = len(self.internalPop)
numAdded = 0
maxTries = 1000
numTries = 0
crossover_empty = self.select(
popID=self.currentGeneration).crossover.isEmpty()
###TODO: enforce constraints!###
while numAdded < popsize:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom,
dad=genomeDad,
count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(
self.pCrossover):
for it in genomeMom.crossover.applyFunctions(
mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
# logging.debug("done cloning")
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
if (numTries > maxTries
or self.owner.eval_constraints(sister)):
newPop.internalPop.append(sister)
numAdded += 1
print "successfully added sister"
if (numAdded < popsize
and (numTries > maxTries
or self.owner.eval_constraints(brother))):
newPop.internalPop.append(brother)
print "successfully added brother"
numAdded += 1
numTries += 1
#end rank0 onlye
# print "rank %d start eval pop" % MPI.COMM_WORLD.Get_rank()
logging.debug("Evaluating the newly created population.")
newPop.evaluate()
# print "rank %d done eval pop" % MPI.COMM_WORLD.Get_rank()
# if (MPI.COMM_WORLD.Get_rank() == 0):
# print "after eval, new pop's positions are:"
# for p in newPop:
# print p.wt_positions
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
if self.elitism:
logging.debug("Doing elitism, %d" % self.nElitismReplacement)
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
# self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score > newPop.bestRaw(
i).score:
newPop[len(newPop) - 1 -
i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
# self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score < newPop.bestRaw(
i).score:
newPop[len(newPop) - 1 -
i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
self.internalPop.sort()
# if (MPI.COMM_WORLD.Get_rank() == 0):
# print "after sort, internal pop's positions are:"
# for p in self.internalPop:
# print p.wt_positions
logging.debug("The generation %d was finished.",
self.currentGeneration)
self.currentGeneration += 1
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
self.saveState()
return (self.currentGeneration == self.nGenerations)
def saveState(self):
self.owner.saveState(self.internalPop.internalPop,
self.currentGeneration)
def restoreState(self, gen):
self.owner.restoreState(self.internalPop.internalPop, gen)
self.currentGeneration = gen
def printStats(self):
""" Print generation statistics
:rtype: the printed statistics as string
.. versionchanged:: 0.6
The return of *printStats* method.
"""
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
percent = self.currentGeneration * 100 / float(self.nGenerations)
message = "Gen. %d (%.2f%%):" % (self.currentGeneration, percent)
logging.info(message)
print message,
sys_stdout.flush()
self.internalPop.statistics()
stat_ret = self.internalPop.printStats()
return message + stat_ret
else:
return ""
def printTimeElapsed(self):
""" Shows the time elapsed since the begin of evolution """
total_time = time() - self.time_init
print "Total time elapsed: %.3f seconds." % total_time
return total_time
def dumpStatsDB(self):
""" Dumps the current statistics to database adapter """
logging.debug("Dumping stats to the DB Adapter")
self.internalPop.statistics()
self.dbAdapter.insert(self)
def evolve(self, freq_stats=0, restore=-1):
""" Do all the generations until the termination criteria, accepts
the freq_stats (default is 0) to dump statistics at n-generation
Example:
>>> ga_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats generation.
:rtype: returns the best individual of the evolution
.. versionadded:: 0.6
the return of the best individual
"""
stopFlagCallback = False
stopFlagTerminationCriteria = False
self.time_init = time()
logging.debug(
"Starting the DB Adapter and the Migration Adapter if any")
if self.dbAdapter: self.dbAdapter.open(self)
if self.migrationAdapter: self.migrationAdapter.start()
if self.getGPMode():
gp_function_prefix = self.getParam("gp_function_prefix")
if gp_function_prefix is not None:
self.__gp_catch_functions(gp_function_prefix)
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
self.initialize()
if (restore >= 0):
self.restoreState(restore)
self.internalPop.evaluate()
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
self.internalPop.sort()
# from mpi4py import MPI
# if (MPI.COMM_WORLD.Get_rank() == 0):
# print "after eval, new pop's positions are:"
# for p in self.internalPop:
# print p.wt_positions
logging.debug("Starting loop over evolutionary algorithm.")
while True:
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
if self.migrationAdapter:
logging.debug("Migration adapter: exchange")
self.migrationAdapter.exchange()
self.internalPop.clearFlags()
self.internalPop.sort()
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats:
if (self.currentGeneration % freq_stats
== 0) or (self.getCurrentGeneration() == 0):
self.printStats()
if self.dbAdapter:
if self.currentGeneration % self.dbAdapter.getStatsGenFreq(
) == 0:
self.dumpStatsDB()
if stopFlagTerminationCriteria:
logging.debug(
"Evolution stopped by the Termination Criteria !")
if freq_stats:
print "\n\tEvolution stopped by Termination Criteria function !\n"
break
if stopFlagCallback:
logging.debug(
"Evolution stopped by Step Callback function !")
if freq_stats:
print "\n\tEvolution stopped by Step Callback function !\n"
break
if self.interactiveMode:
if sys_platform[:3] == "win":
if msvcrt.kbhit():
if ord(msvcrt.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive Mode...",
logging.debug(
"Windows Interactive Mode key detected ! generation=%d",
self.getCurrentGeneration())
from pyevolve import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##\nPress CTRL-Z to quit interactive mode." % (
pyevolve.__version__, )
session_locals = {
"ga_engine": self,
"population": self.getPopulation(),
"pyevolve": pyevolve,
"it": Interaction
}
print
code.interact(interact_banner,
local=session_locals)
if (self.getInteractiveGeneration() >=
0) and (self.getInteractiveGeneration()
== self.getCurrentGeneration()):
print "Loading modules for Interactive Mode...",
logging.debug(
"Manual Interactive Mode key detected ! generation=%d",
self.getCurrentGeneration())
from pyevolve import Interaction
print " done !"
interact_banner = "## Pyevolve v.%s - Interactive Mode ##" % (
pyevolve.__version__, )
session_locals = {
"ga_engine": self,
"population": self.getPopulation(),
"pyevolve": pyevolve,
"it": Interaction
}
print
code.interact(interact_banner, local=session_locals)
## end, rank0 only
if self.step(): break
if (MPI.COMM_WORLD.Get_rank() == 0
): ### assumes WE are on top of hierarchy!
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
if self.dbAdapter:
logging.debug("Closing the DB Adapter")
if not (self.currentGeneration %
self.dbAdapter.getStatsGenFreq() == 0):
self.dumpStatsDB()
self.dbAdapter.commitAndClose()
if self.migrationAdapter:
logging.debug("Closing the Migration Adapter")
if freq_stats: print "Stopping the migration adapter... ",
self.migrationAdapter.stop()
if freq_stats: print "done !"
return self.bestIndividual()
else:
return None
def select(self, **args):
""" Select one individual from population
:param args: this parameters will be sent to the selector
"""
for it in self.selector.applyFunctions(self.internalPop, **args):
return it
class SimplePSO(object):
""" SimplePSO Engine Class - The PSO Algorithm Core
Example:
>>> topology = Topology.GlobalTopology(particle_rep)
>>> pso = PSO.SimplePSO(topology)
>>> pso.setSteps(120)
:param topology: the :term:`Sample Topology``
:param interactiveMode: this flag enables the Interactive Mode
:param seed: the random seed value
.. note:: if you see the same random seed, all the runs of the algorithm will be the same.
"""
stepCallBack = None
""" This is the the :term: `step callback function` slot,
if you want to set the function, you must do this: ::
def your_func(pso_engine):
#Here you have access to the PSO Engine
return False
pso_engine.stepCallback.set(your_func)
now *"your_func"* will be called every step.
When this function returns True, the PSO Engine will stop the evolution and show
a warning, if is False, the evolution continues.
"""
terminationCriteria = None
""" This is the termination criteria slot, if you want to set one
termination criteria, you mus do this: ::
pso_engine.terminationCriteria.set(your_func)
Now, when you run your PSO, it will stop when terminationCriteria be satisfied.
To create your own termination function, you must put at least one parameter
which is the PSO Engine, follows an example: ::
def ConvergenceCriteria(pso_engine):
swarm = pso_engine.getSwarm()
return swarm[0] == swarm[len(swarm)-1]
When this function returns True, the Pso Engine will stop the evolution and show
a warning. If is False, the evolution continues, this function is called every
step.
"""
def __init__(self, topology, seed=None, interactiveMode=True):
""" Initializator of PSO """
#random seed
random.seed(seed)
#Pso type used by the particle
self.psoType = Consts.CDefPsoType
#Topology used
self.topology = topology
#Set the population size
self.setSwarmSize(Consts.CDefSwarmSize)
#Cognitive and Social Coefficients
self.C1, self.C2 = Consts.CDefCoefficients
#Time steps
self.timeSteps = Consts.CDefSteps
#Interactive Mode (True or False)
self.interactiveMode = interactiveMode
#Current step
self.currentStep = 0
#Inertia Factor Minus
self.inertiaFactorMinus = None
#Inertia coefficient
self.inertiaFactor = None
#Time initial
self.time_init = None
#Optimization type
self.minimax = Consts.minimaxType["minimize"]
#Report file adapter
self.reportAdapter = None
#Step Callback
self.stepCallback = FunctionSlot("Step Callback")
#Termination Criteria
self.terminationCriteria = FunctionSlot("Termination Criteria")
#All slots
self.allSlots = [self.stepCallback, self.terminationCriteria]
print "A PSO Engine was created, timeSteps=% d" % (self.timeSteps, )
def __repr__(self):
""" The String representation of the PSO Engine """
ret = "- PSO-%s-%s Execution\n" % (self.getTopologyType(),
self.getPsoType())
ret += "\tSwarm Size:\t %d\n" % (self.topology.swarmSize, )
ret += "\tTime Steps:\t %d\n" % (self.timeSteps, )
ret += "\tCurrent Step:\t %d\n" % (self.currentStep, )
ret += "\tMinimax Type:\t %s\n" % (Consts.minimaxType.keys()[
Consts.minimaxType.values().index(self.minimax)].capitalize(), )
ret += "\tReport Adapter:\t %s\n" % (self.reportAdapter, )
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
return ret
def setReportAdapter(self, repadapter):
""" Sets the Report Adapter of the PSO Engine
:param repadapter: one of the :mod:`ReportAdapters` classes instance
.. warning: the use of a Report Adapter can reduce the speed performance of the PSO.
"""
self.reportAdapter = repadapter
def setSwarmSize(self, size):
""" Sets the swarm size, calls setSwarmSize() of Topology
:param size: the swarm size
.. note:: the swarm size must be >= 2
"""
if size < 2:
Util.raiseException("swarm size must be >= 2", ValueError)
self.topology.setSwarmSize(size)
def setPsoType(self, psoType):
""" Sets the psoType, use Consts.psoType(Basic,Constricted,Inertia)
Example:
>>> pso_engine.setSortType(Consts.psoType["CONSTRICTED"])
:param psoType: The PSO type, from Consts.psoType
"""
if psoType not in Consts.psoType.values():
Util.raiseException("PsoType must be implemented !", TypeError)
self.psoType = psoType
def getPsoType(self):
""" Return the Pso Type
:rtype key: pso Type
"""
for key, value in Consts.psoType.items():
if value == self.psoType:
return key
return ""
def setTimeSteps(self, num_steps):
""" Sets the number of steps to converge
:param num_steps: the number of steps
"""
if num_steps < 1:
Util.raiseException("Number of steps must be >=1", ValueError)
self.timeSteps = num_steps
def getMinimax(self):
""" Gets the minimize/maximize mode
:rtype: The Consts.minimaxType type
"""
for key, value in Consts.minimaxType.items():
if value == self.minimax:
return key
return ""
def getTopologyType(self):
""" Returns the name of the topology
:rtype name: the name of the topology
"""
return self.topology.__class__.__name__
def setMinimax(self, minimax):
"""Sets the minimize/maximize mode, use Consts.minimaxType
:param minimax: the minimax mode, from Consts.minimaxType
"""
if minimax not in Consts.minimaxType.values():
Util.raiseException(
"Optimization type must be Maximize or Minimize !", TypeError)
self.minimax = minimax
def getCurrentStep(self):
""" Gets the current step
:rtype: the current step
"""
return self.currentStep
def getReportAdapter(self):
""" Gets the Report Adapter of the PSO Engine
:rtype: a instance from one of the :mod:`ReportAdapters` classes
"""
return self.reportAdapter
def bestParticle(self):
""" Returns the swarm best Particle
:rtype: the best particle
"""
return self.topology.getBestParticle()
def getTopology(self):
"""Return the internal topology of Pso Engine
:rtype: the topology (:class: 'Topology.Topology')'
"""
return self.topology
def getStatistics(self):
""" Gets the Statistics class instance of the current step
:rtype: the statistics instance (:class: `Statistics.Statistics`)`
"""
return self.topology.getStatistics()
def dumpStatsReport(self):
""" Dumps the current statistics to the report adapter """
self.topology.statistics()
self.reportAdapter.insert(self.getStatistics(), self.topology,
self.currentStep)
def printStats(self):
""" Print swarm statistics"""
percent = self.currentStep * 100 / float(self.timeSteps)
message = "Step: %d (%.2f%%):" % (self.currentStep, percent)
print message
self.topology.statistics()
self.topology.printStats()
def printTimeElapsed(self):
""" Shows the time elapsed since the beginning of the solution construction """
print "Total time elapsed: %.3f seconds." % (time() - self.time_init)
def initialize(self):
""" Initializes the PSO Engine. Create and initialize the swarm """
self.topology.create(minimax=self.minimax)
self.topology.initialize()
print "The PSO Engine was initialized !"
def constructSolution(self):
""" Just do one step in execution, one step."""
for it in self.topology.position_updater.applyFunctions(self):
pass
for it in self.topology.information_updater.applyFunctions(self):
pass
if self.psoType == Consts.psoType["INERTIA"]:
self.updateInertiaFactor()
self.currentStep += 1
return (self.currentStep == self.timeSteps)
def execute(self, freq_stats=0):
""" Do all the steps until the termination criteria or time Steps achieved,
accepts the freq_stats (default is 0) to dump statistics at n-step
Example:
>>> pso_engine.evolve(freq_stats=10)
(...)
:param freq_stats: if greater than 0, the statistics will be
printed every freq_stats step.
"""
#Start time
self.time_init = time()
#Creates a new report if reportAdapter is not None.
if self.reportAdapter: self.reportAdapter.open()
#Initialize the PSO Engine
self.initialize() #Already evaluates all particles
print "Starting loop over evolutionary algorithm."
try:
while not self.constructSolution():
stopFlagCallback = False
stopFlagTerminationCriteria = False
if not self.stepCallback.isEmpty():
for it in self.stepCallback.applyFunctions(self):
stopFlagCallback = it
if not self.terminationCriteria.isEmpty():
for it in self.terminationCriteria.applyFunctions(self):
stopFlagTerminationCriteria = it
if freq_stats != 0:
if (self.currentStep % freq_stats
== 0) or (self.currentStep == 1):
self.printStats()
if self.reportAdapter:
if self.currentStep % self.reportAdapter.statsGenFreq == 0:
self.dumpStatsReport()
if stopFlagTerminationCriteria:
print '\n\tExecution stopped by Termination Criteria function !\n'
break
if stopFlagCallback:
print '\n\tExecution stopped by Step Callback function!\n'
break
if self.interactiveMode:
if sys_platform[:3] == "win":
if msvcrt.kbhit():
if ord(msvcrt.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive mode...",
import pypso.Interaction
print "done!\n"
interact_banner = "## PyPSO v.%s - Interactive Mode ##\nPress CTRL-Z to quit interactive mode." % (
pypso.__version__, )
session_locals = {
"pso_engine": self,
"topology": self.getTopology(),
"swarm_statistics":
self.getTopology().swarmStats,
"topology_statistics":
self.getTopology().topologyStats,
"pypso": pypso,
"it": pypso.Interaction
}
print
code.interact(interact_banner,
local=session_locals)
elif sys_platform[:5] == "linux":
if Util.kbhit():
if ord(Util.getch()) == Consts.CDefESCKey:
print "Loading modules for Interactive mode...",
import pypso.Interaction
print "done!\n"
interact_banner = "## PyPSO v.%s - Interactive Mode ##\nPress CTRL-D to quit interactive mode." % (
pypso.__version__, )
session_locals = {
"pso_engine": self,
"topology": self.getTopology(),
"swarm_statistics":
self.getTopology().swarmStats,
"topology_statistics":
self.getTopology().topologyStats,
"pypso": pypso,
"it": pypso.Interaction
}
print
code.interact(interact_banner,
local=session_locals)
except KeyboardInterrupt:
print "\n\tA break was detected, you have interrupted the evolution !\n"
if freq_stats != 0:
self.printStats()
self.printTimeElapsed()
if self.reportAdapter:
if (self.currentStep % self.reportAdapter.statsGenFreq == 0):
self.dumpStatsReport()
self.reportAdapter.saveAndClose()
class GenomeBase:
""" GenomeBase Class - The base of all chromosome representation """
evaluator = None
""" This is the :term:`evaluation function` slot, you can add
a function with the *set* method: ::
genome.evaluator.set(eval_func)
"""
initializator = None
""" This is the initialization function of the genome, you
can change the default initializator using the function slot: ::
genome.initializator.set(Initializators.G1DListInitializatorAllele)
In this example, the initializator :func:`Initializators.G1DListInitializatorAllele`
will be used to create the initial population.
"""
mutator = None
""" This is the mutator function slot, you can change the default
mutator using the slot *set* function: ::
genome.mutator.set(Mutators.G1DListMutatorSwap)
"""
crossover = None
""" This is the reproduction function slot, the crossover. You
can change the default crossover method using: ::
genome.crossover.set(Crossovers.G1DListCrossoverUniform)
"""
def __init__(self):
"""Genome Constructor"""
self.evaluator = FunctionSlot("Evaluator")
self.initializator = FunctionSlot("Initializator")
self.mutator = FunctionSlot("Mutator")
self.crossover = FunctionSlot("Crossover")
self.internalParams = {}
self.score = 0.0
self.fitness = 0.0
def getRawScore(self):
""" Get the Raw Score of the genome
:rtype: genome raw score
"""
return self.score
def getFitnessScore(self):
""" Get the Fitness Score of the genome
:rtype: genome fitness score
"""
return self.fitness
def __repr__(self):
"""String representation of Genome"""
allSlots = self.allSlots = [
self.evaluator, self.initializator, self.mutator, self.crossover
]
ret = "- GenomeBase\n"
ret += "\tScore:\t\t\t %.6f\n" % (self.score, )
ret += "\tFitness:\t\t %.6f\n\n" % (self.fitness, )
ret += "\tParams:\t\t %s\n\n" % (self.internalParams, )
for slot in allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
return ret
def setParams(self, **args):
""" Set the internal params
Example:
>>> genome.setParams(rangemin=0, rangemax=100, gauss_mu=0, gauss_sigma=1)
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param args: this params will saved in every chromosome for genetic op. use
"""
self.internalParams.update(args)
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> genome.getParam("rangemax")
100
.. note:: All the individuals of the population shares this parameters and uses
the same instance of this dict.
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def resetStats(self):
""" Clear score and fitness of genome """
self.score = 0.0
self.fitness = 0.0
def evaluate(self, **args):
""" Called to evaluate genome
:param args: this parameters will be passes to the evaluator
"""
self.resetStats()
for it in self.evaluator.applyFunctions(self, **args):
self.score += it
def initialize(self, **args):
""" Called to initialize genome
:param args: this parameters will be passed to the initializator
"""
for it in self.initializator.applyFunctions(self, **args):
pass
def mutate(self, **args):
""" Called to mutate the genome
:param args: this parameters will be passed to the mutator
:rtype: the number of mutations returned by mutation operator
"""
nmuts = 0
for it in self.mutator.applyFunctions(self, **args):
nmuts += it
return nmuts
def copy(self, g):
""" Copy the current GenomeBase to 'g'
:param g: the destination genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
g.score = self.score
g.fitness = self.fitness
g.evaluator = self.evaluator
g.initializator = self.initializator
g.mutator = self.mutator
g.crossover = self.crossover
#g.internalParams = self.internalParams.copy()
g.internalParams = self.internalParams
def clone(self):
""" Clone this GenomeBase
:rtype: the clone genome
.. note:: If you are planning to create a new chromosome representation, you
**must** implement this method on your class.
"""
newcopy = GenomeBase()
self.copy(newcopy)
return newcopy
class GPopulation(object):
""" GPopulation Class - The container for the population
**Examples**
Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
>>> pop = ga_engine.getPopulation()
Get the best fitness individual
>>> bestIndividual = pop.bestFitness()
Get the best raw individual
>>> bestIndividual = pop.bestRaw()
Get the statistics from the :class:`Statistics.Statistics` instance
>>> stats = pop.getStatistics()
>>> print stats["rawMax"]
10.4
Iterate, get/set individuals
>>> for ind in pop:
>>> print ind
(...)
>>> for i in xrange(len(pop)):
>>> print pop[i]
(...)
>>> pop[10] = newGenome
>>> pop[10].fitness
12.5
:param genome: the :term:`Sample genome`, or a GPopulation object, when cloning.
"""
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.internalParams = genome.internalParams
self.multiProcessing = genome.multiProcessing
self.statted = False
self.stats = Statistics()
return
logging.debug("New population instance, %s class genomes.",
genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.internalParams = {}
self.multiProcessing = (False, False, None)
# Statistics
self.statted = False
self.stats = Statistics()
def setMultiProcessing(self,
flag=True,
full_copy=False,
max_processes=None):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
:param max_processes: None (default) or an integer value
.. warning:: Use this option only when your evaluation function is slow, se you
will get a good tradeoff between the process communication speed and the
parallel evaluation.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
self.multiProcessing = (flag, full_copy, max_processes)
def setMinimax(self, minimax):
""" Sets the population minimax
Example:
>>> pop.setMinimax(Consts.minimaxType["maximize"])
:param minimax: the minimax type
"""
self.minimax = minimax
def __repr__(self):
""" Returns the string representation of the population """
ret = "- GPopulation\n"
ret += "\tPopulation Size:\t %d\n" % (self.popSize, )
ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[
Consts.sortType.values().index(self.sortType)].capitalize(), )
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[
Consts.minimaxType.values().index(self.minimax)].capitalize(), )
for slot in self.allSlots:
ret += "\t" + slot.__repr__()
ret += "\n"
ret += self.stats.__repr__()
return ret
def __len__(self):
""" Return the length of population """
return len(self.internalPop)
def __getitem__(self, key):
""" Returns the specified individual from population """
return self.internalPop[key]
def __iter__(self):
""" Returns the iterator of the population """
return iter(self.internalPop)
def __setitem__(self, key, value):
""" Set an individual of population """
self.internalPop[key] = value
self.clearFlags()
def clearFlags(self):
""" Clear the sorted and statted internal flags """
self.sorted = False
self.statted = False
def getStatistics(self):
""" Return a Statistics class for statistics
:rtype: the :class:`Statistics.Statistics` instance
"""
self.statistics()
return self.stats
def statistics(self):
""" Do statistical analysis of population and set 'statted' to True """
if self.statted:
return
logging.debug("Running statistical calculations")
raw_sum = 0
len_pop = len(self)
for ind in xrange(len_pop):
raw_sum += self[ind].score
self.stats["rawMax"] = max(self, key=key_raw_score).score
self.stats["rawMin"] = min(self, key=key_raw_score).score
self.stats["rawAve"] = raw_sum / float(len_pop)
tmpvar = 0.0
for ind in xrange(len_pop):
s = self[ind].score - self.stats["rawAve"]
s *= s
tmpvar += s
tmpvar /= float((len(self) - 1))
try:
self.stats["rawDev"] = math_sqrt(tmpvar)
except:
self.stats["rawDev"] = 0.0
self.stats["rawVar"] = tmpvar
self.statted = True
def bestFitness(self, index=0):
""" Return the best scaled fitness individual of population
:param index: the *index* best individual
:rtype: the individual
"""
self.sort()
return self.internalPop[index]
def worstFitness(self):
""" Return the worst scaled fitness individual of the population
:rtype: the individual
"""
self.sort()
return self.internalPop[-1]
def bestRaw(self, index=0):
""" Return the best raw score individual of population
:param index: the *index* best raw individual
:rtype: the individual
.. versionadded:: 0.6
The parameter `index`.
"""
if self.sortType == Consts.sortType["raw"]:
return self.internalPop[index]
else:
self.sort()
return self.internalPopRaw[index]
def worstRaw(self):
""" Return the worst raw score individual of population
:rtype: the individual
.. versionadded:: 0.6
The parameter `index`.
"""
if self.sortType == Consts.sortType["raw"]:
return self.internalPop[-1]
else:
self.sort()
return self.internalPopRaw[-1]
def sort(self):
""" Sort the population """
if self.sorted:
return
rev = (self.minimax == Consts.minimaxType["maximize"])
if self.sortType == Consts.sortType["raw"]:
self.internalPop.sort(cmp=Util.cmp_individual_raw, reverse=rev)
else:
self.scale()
self.internalPop.sort(cmp=Util.cmp_individual_scaled, reverse=rev)
self.internalPopRaw = self.internalPop[:]
self.internalPopRaw.sort(cmp=Util.cmp_individual_raw, reverse=rev)
self.sorted = True
def setPopulationSize(self, size):
""" Set the population size
:param size: the population size
"""
self.popSize = size
def setSortType(self, sort_type):
""" Sets the sort type
Example:
>>> pop.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
self.sortType = sort_type
def create(self, **args):
""" Clone the example genome to fill the population """
self.minimax = args["minimax"]
self.internalPop = [
self.oneSelfGenome.clone() for i in xrange(self.popSize)
]
self.clearFlags()
def __findIndividual(self, individual, end):
for i in xrange(end):
if individual.compare(self.internalPop[i]) == 0:
return True
def initialize(self, **args):
""" Initialize all individuals of population,
this calls the initialize() of individuals """
logging.debug("Initializing the population")
if self.oneSelfGenome.getParam("full_diversity", True) and hasattr(
self.oneSelfGenome, "compare"):
for i in xrange(len(self.internalPop)):
curr = self.internalPop[i]
curr.initialize(**args)
while self.__findIndividual(curr, i):
curr.initialize(**args)
else:
for gen in self.internalPop:
gen.initialize(**args)
self.clearFlags()
def evaluate(self, **args):
""" Evaluate all individuals in population, calls the evaluate() method of individuals
:param args: this params are passed to the evaluation function
"""
# We have multiprocessing
if self.multiProcessing[0] and MULTI_PROCESSING:
logging.debug(
"Evaluating the population using the multiprocessing method")
proc_pool = Pool(processes=self.multiProcessing[2])
# Multiprocessing full_copy parameter
if self.multiProcessing[1]:
results = proc_pool.map(multiprocessing_eval_full,
self.internalPop)
proc_pool.close()
proc_pool.join()
for i in xrange(len(self.internalPop)):
self.internalPop[i] = results[i]
else:
results = proc_pool.map(multiprocessing_eval, self.internalPop)
proc_pool.close()
proc_pool.join()
for individual, score in zip(self.internalPop, results):
individual.score = score
else:
for ind in self.internalPop:
ind.evaluate(**args)
self.clearFlags()
def scale(self, **args):
""" Scale the population using the scaling method
:param args: this parameter is passed to the scale method
"""
for it in self.scaleMethod.applyFunctions(self, **args):
pass
fit_sum = 0
for ind in xrange(len(self)):
fit_sum += self[ind].fitness
self.stats["fitMax"] = max(self, key=key_fitness_score).fitness
self.stats["fitMin"] = min(self, key=key_fitness_score).fitness
self.stats["fitAve"] = fit_sum / float(len(self))
self.sorted = False
def printStats(self):
""" Print statistics of the current population """
message = ""
if self.sortType == Consts.sortType["scaled"]:
message = "Max/Min/Avg Fitness(Raw) [%(fitMax).2f(%(rawMax).2f)/%(fitMin).2f(%(rawMin).2f)/%(fitAve).2f(%(rawAve).2f)]" % self.stats
else:
message = "Max/Min/Avg Raw [%(rawMax).2f/%(rawMin).2f/%(rawAve).2f]" % self.stats
logging.info(message)
print message
return message
def copy(self, pop):
""" Copy current population to 'pop'
:param pop: the destination population
.. warning:: this method do not copy the individuals, only the population logic
"""
pop.popSize = self.popSize
pop.sortType = self.sortType
pop.minimax = self.minimax
pop.scaleMethod = self.scaleMethod
pop.internalParams = self.internalParams
pop.multiProcessing = self.multiProcessing
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> population.getParam("tournamentPool")
5
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def setParams(self, **args):
""" Gets an internal parameter
Example:
>>> population.setParams(tournamentPool=5)
:param args: parameters to set
.. versionadded:: 0.6
The `setParams` method.
"""
self.internalParams.update(args)
def clear(self):
""" Remove all individuals from population """
del self.internalPop[:]
del self.internalPopRaw[:]
self.clearFlags()
def clone(self):
""" Return a brand-new cloned population """
newpop = GPopulation(self.oneSelfGenome)
self.copy(newpop)
return newpop
