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 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)