/usr/lib/python2.7/dist-packages/pyevolve/GPopulation.py is in python-pyevolve 0.6~rc1+svn398+dfsg-9.
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:mod:`GPopulation` -- the population module
================================================================
This module contains the :class:`GPopulation.GPopulation` class, which is reponsible
to keep the population and the statistics.
Default Parameters
-------------------------------------------------------------
*Sort Type*
>>> Consts.sortType["scaled"]
The scaled sort type
*Minimax*
>>> Consts.minimaxType["maximize"]
Maximize the evaluation function
*Scale Method*
:func:`Scaling.LinearScaling`
The Linear Scaling scheme
Class
-------------------------------------------------------------
"""
import Consts, Util
from FunctionSlot import FunctionSlot
from Statistics import Statistics
from math import sqrt as math_sqrt
import logging
try:
from multiprocessing import cpu_count, Pool
CPU_COUNT = cpu_count()
MULTI_PROCESSING = True if CPU_COUNT > 1 else False
logging.debug("You have %d CPU cores, so the multiprocessing state is %s", CPU_COUNT, MULTI_PROCESSING)
except ImportError:
MULTI_PROCESSING = False
logging.debug("You don't have multiprocessing support for your Python version !")
def key_raw_score(individual):
""" A key function to return raw score
:param individual: the individual instance
:rtype: the individual raw score
.. note:: this function is used by the max()/min() python functions
"""
return individual.score
def key_fitness_score(individual):
""" A key function to return fitness score, used by max()/min()
:param individual: the individual instance
:rtype: the individual fitness score
.. note:: this function is used by the max()/min() python functions
"""
return individual.fitness
def multiprocessing_eval(ind):
""" Internal used by the multiprocessing """
ind.evaluate()
return ind.score
def multiprocessing_eval_full(ind):
""" Internal used by the multiprocessing (full copy)"""
ind.evaluate()
return ind
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.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)
# Statistics
self.statted = False
self.stats = Statistics()
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.
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, 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)
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")
proc_pool = Pool()
# Multiprocessing full_copy parameter
if self.multiProcessing[1]:
results = proc_pool.map(multiprocessing_eval_full, self.internalPop)
for i in xrange(len(self.internalPop)):
self.internalPop[i] = results[i]
else:
results = proc_pool.map(multiprocessing_eval, self.internalPop)
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.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
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