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, genome, seed=None, interactiveMode=True):
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
pyevolve.Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
pyevolve.Util.raiseException(
"The genome must be a GenomeBase subclass", TypeError)
self.internalPop = self.make_population(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, genome, seed=None, interactiveMode=True):
""" Initializator of GSimpleGA """
if seed:
random.seed(seed)
if type(interactiveMode) != bool:
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.max_time = 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):
"""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, 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 __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()
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(key=cmp_to_key(Util.cmp_individual_raw), reverse=rev)
else:
self.scale()
self.internalPop.sort(key=cmp_to_key(Util.cmp_individual_scaled), reverse=rev)
self.internalPopRaw = self.internalPop[:]
self.internalPopRaw.sort(key=cmp_to_key(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
class GSimpleGA(object):
""" 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 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 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) != bool:
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.max_time = 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) != bool:
Util.raiseException("Interactive Mode option must be True or False", TypeError)
self.interactiveMode = flag
def __repr__(self):
""" The string representation of the GA Engine """
minimax_type = list(Consts.minimaxType.keys())[list(Consts.minimaxType.values()).index(self.minimax)]
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" % minimax_type.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, 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.
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 max_processes: None (default) or an integer value
.. 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) != bool:
Util.raiseException("Multiprocessing option must be True or False", TypeError)
if type(full_copy) != bool:
Util.raiseException("Multiprocessing 'full_copy' option must be True or False", TypeError)
self.internalPop.setMultiProcessing(flag, full_copy, max_processes)
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) != bool:
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 setMaxTime(self, seconds):
""" Sets the maximun evolve time of the GA Engine
:param seconds: maximum time in seconds
"""
self.max_time = seconds
def getMaxTime(self):
""" Get the maximun evolve time of the GA Engine
:rtype: True or False
"""
return self.max_time
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 """
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
if self.max_time:
total_time = time() - self.time_init
if total_time > self.max_time:
return True
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.")
try:
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()
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 ##\n" \
"Press 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)
is_interactive_generation = self.getInteractiveGeneration() == self.getCurrentGeneration()
if self.getInteractiveGeneration() >= 0 and is_interactive_generation:
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
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()
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")
self.migrationAdapter.stop()
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 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 SimpleGAWithFixedElitism(pyevolve.GSimpleGA.GSimpleGA):
# "Reimplementation" of GSimpleGA with ability to create different
# population types (used by SimpleMPIGA), and fixed elitism where elite
# individuals also have their fitness evaluated at each generation.
def __init__(self, genome, seed=None, interactiveMode=True):
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
pyevolve.Util.raiseException("Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
pyevolve.Util.raiseException("The genome must be a GenomeBase subclass", TypeError)
self.internalPop = self.make_population(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 step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = self.make_population(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 pyevolve.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 pyevolve.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)
#Niching methods- Petrowski's clearing
self.clear()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in range(self.nElitismReplacement):
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in range(self.nElitismReplacement):
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
# Evalate after elitism, in order to re-evaluate elite individuals on
# potentially changed environment.
logging.debug("Evaluating the new created population.")
newPop.evaluate()
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)
def make_population(self, genome):
return SpecifiedPopulation(genome)
class GSimpleGA(object):
""" 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 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 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) != bool:# 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.max_time = 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) != bool :#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 """
minimax_type = Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)]
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" % minimax_type.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, 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.
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 max_processes: None (default) or an integer value
.. 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, max_processes)
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 setMaxTime(self, seconds):
""" Sets the maximun evolve time of the GA Engine
:param seconds: maximum time in seconds
"""
self.max_time = seconds
def getMaxTime(self):
""" Get the maximun evolve time of the GA Engine
:rtype: True or False
"""
return self.max_time
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 """
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 range(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 range(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 range(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
if self.max_time:
total_time = time() - self.time_init
if total_time > self.max_time:
return True
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.")
try:
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()
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 ##\n" \
"Press 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)
is_interactive_generation = self.getInteractiveGeneration() == self.getCurrentGeneration()
if self.getInteractiveGeneration() >= 0 and is_interactive_generation:
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
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()
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")
self.migrationAdapter.stop()
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 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
class SimpleGAWithFixedElitism(pyevolve.GSimpleGA.GSimpleGA):
# "Reimplementation" of GSimpleGA with ability to create different
# population types (used by SimpleMPIGA), and fixed elitism where elite
# individuals also have their fitness evaluated at each generation.
def __init__(self, genome, seed=None, interactiveMode=True):
if seed: random.seed(seed)
if type(interactiveMode) != BooleanType:
pyevolve.Util.raiseException(
"Interactive Mode option must be True or False", TypeError)
if not isinstance(genome, GenomeBase):
pyevolve.Util.raiseException(
"The genome must be a GenomeBase subclass", TypeError)
self.internalPop = self.make_population(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 step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = self.make_population(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 pyevolve.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 pyevolve.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)
#Niching methods- Petrowski's clearing
self.clear()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in range(self.nElitismReplacement):
newPop[len(newPop) - 1 - i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in range(self.nElitismReplacement):
newPop[len(newPop) - 1 - i] = self.internalPop.bestRaw(i)
# Evalate after elitism, in order to re-evaluate elite individuals on
# potentially changed environment.
logging.debug("Evaluating the new created population.")
newPop.evaluate()
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.",
self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)
def make_population(self, genome):
return SpecifiedPopulation(genome)
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
