import argparse import json import os import pickle import time import math import random from abc import ABC, abstractmethod from ..base.random_sequence_index import RandomIndexSequence from ..constants import BAD_FITNESS from ..json_encoders import Encoder from ..structures.hall_of_fame import HallOfFame from ..structures.individual import Individual from ..structures.population import PopulationStructures class ExperimentABC(ABC): def __init__(self, popsize, hof_size, save_only_best) -> None: self.population_structures = None self.stats = [] self.current_generation = 0 self.time_elapsed = 0 self.hof = HallOfFame(hof_size) self.popsize = popsize self.save_only_best = save_only_best def select(self, individuals, tournament_size, random_index_sequence): """Tournament selection, returns the index of the best individual from those taking part in the tournament""" best_index = None for i in range(tournament_size): rnd_index = random_index_sequence.getNext() if best_index is None or individuals[rnd_index].fitness > best_index.fitness: best_index = individuals[rnd_index] return best_index def addGenotypeIfValid(self, ind_list, genotype): new_individual = Individual() new_individual.set_and_evaluate(genotype, self.evaluate) if new_individual.fitness is not BAD_FITNESS: ind_list.append(new_individual) @staticmethod def stochastic_round(value): # https://en.wikipedia.org/wiki/Rounding#Stochastic_rounding # For example, value==2.1 should turn in most cases to int 2, rarely to int 3 lower = math.floor(value) # returns int return lower + (random.random() < (value - lower)) def make_new_population(self, individuals, prob_mut, prob_xov, tournament_size): """'individuals' is the input population (a list of individuals). Assumptions: all genotypes in 'individuals' are valid and evaluated (have fitness set). Returns: a new population of size 'self.popsize' with prob_mut mutants, prob_xov offspring, and the remainder of clones.""" # if (self.popsize * probability) below is not integer (e.g. popsize=50, prob_xov=0.333, expected number of crossovers = 50*0.333=16.65), stochastic_round() will ensure that you will get on average the expected number of crossovers per generation (e.g. 16.65: less often 16, more often 17). expected_mut = self.stochastic_round(self.popsize * prob_mut) # or int(...) if you accept less mutants in some cases, see the comment above expected_xov = self.stochastic_round(self.popsize * prob_xov) # or int(...) if you accept less crossovers in some cases, see the comment above assert expected_mut + expected_xov <= self.popsize, "If probabilities of mutation (%g) and crossover (%g) added together exceed 1.0, then the population would grow every generation..." % (prob_mut, prob_xov) # can be triggered due to stochastic_round() if prob_mut+prob_xov is close to 1 and the expected number of mutants or crossovers is not integer; if this happens, adjust popsize, prob_mut and prob_xov accordingly. newpop = [] ris = RandomIndexSequence(len(individuals)) # adding valid mutants of selected individuals... while len(newpop) < expected_mut: ind = self.select(individuals, tournament_size=tournament_size, random_index_sequence=ris) self.addGenotypeIfValid(newpop, self.mutate(ind.genotype)) # adding valid crossovers of selected individuals... while len(newpop) < expected_mut + expected_xov: ind1 = self.select(individuals, tournament_size=tournament_size, random_index_sequence=ris) ind2 = self.select(individuals, tournament_size=tournament_size, random_index_sequence=ris) self.addGenotypeIfValid(newpop, self.cross_over(ind1.genotype, ind2.genotype)) # select clones to fill up the new population until we reach the same size as the input population while len(newpop) < self.popsize: ind = self.select(individuals, tournament_size=tournament_size, random_index_sequence=ris) newpop.append(Individual().copyFrom(ind)) return newpop def save_state(self, state_filename): state = self.get_state() if state_filename is None: return state_filename_tmp = state_filename + ".tmp" try: with open(state_filename_tmp, "wb") as f: pickle.dump(state, f) # ensures the new file was first saved OK (e.g. enough free space on device), then replace os.replace(state_filename_tmp, state_filename) except Exception as ex: raise RuntimeError("Failed to save evolution state '%s' (because: %s). This does not prevent the experiment from continuing, but let's stop here to fix the problem with saving state files." % ( state_filename_tmp, ex)) def load_state(self, state_filename): if state_filename is None: # print("Loading evolution state: file name not provided") return None try: with open(state_filename, 'rb') as f: state = pickle.load(f) self.set_state(state) except FileNotFoundError: return None print("...Loaded evolution state from '%s'" % state_filename) return True def get_state_filename(self, save_file_name): return None if save_file_name is None else save_file_name + '_state.pkl' def get_state(self): return [self.time_elapsed, self.current_generation, self.population_structures, self.hof, self.stats] def set_state(self, state): self.time_elapsed, self.current_generation, self.population_structures, hof_, self.stats = state # sorting: ensure that we add from worst to best so all individuals are added to HOF for h in sorted(hof_, key=lambda x: x.rawfitness): self.hof.add(h) def update_stats(self, generation, all_individuals): worst = min(all_individuals, key=lambda item: item.rawfitness) best = max(all_individuals, key=lambda item: item.rawfitness) # instead of single best, could add all individuals in population here, but then the outcome would depend on the order of adding self.hof.add(best) self.stats.append(best.rawfitness if self.save_only_best else best) print("%d\t%d\t%g\t%g" % (generation, len(all_individuals), worst.rawfitness, best.rawfitness)) def initialize_evolution(self, initialgenotype): self.current_generation = 0 self.time_elapsed = 0 self.stats = [] # stores the best individuals, one from each generation initial_individual = Individual() initial_individual.set_and_evaluate(initialgenotype, self.evaluate) self.hof.add(initial_individual) self.stats.append( initial_individual.rawfitness if self.save_only_best else initial_individual) self.population_structures = PopulationStructures( initial_individual=initial_individual, archive_size=0, popsize=self.popsize) def evolve(self, hof_savefile, generations, initialgenotype, pmut, pxov, tournament_size): file_name = self.get_state_filename(hof_savefile) state = self.load_state(file_name) if state is not None: # loaded state from file # saved generation has been completed, start with the next one self.current_generation += 1 print("...Resuming from saved state: population size = %d, hof size = %d, stats size = %d, archive size = %d, generation = %d/%d" % (len(self.population_structures.population), len(self.hof), len(self.stats), (len(self.population_structures.archive)), self.current_generation, generations)) # self.current_generation (and g) are 0-based, parsed_args.generations is 1-based else: self.initialize_evolution(initialgenotype) time0 = time.process_time() for g in range(self.current_generation, generations): self.population_structures.population = self.make_new_population( self.population_structures.population, pmut, pxov, tournament_size) self.update_stats(g, self.population_structures.population) if hof_savefile is not None: self.current_generation = g self.time_elapsed += time.process_time() - time0 self.save_state(file_name) if hof_savefile is not None: self.save_genotypes(hof_savefile) return self.population_structures.population, self.stats @abstractmethod def mutate(self, gen1): pass @abstractmethod def cross_over(self, gen1, gen2): pass @abstractmethod def evaluate(self, genotype): pass def save_genotypes(self, filename): """Implement if you want to save finall genotypes,in default implementation this function is run once at the end of evolution""" state_to_save = { "number_of_generations": self.current_generation, "hof": [{"genotype": individual.genotype, "fitness": individual.rawfitness} for individual in self.hof.hof]} with open(f"{filename}.json", 'w') as f: json.dump(state_to_save, f, cls=Encoder) @staticmethod def get_args_for_parser(): parser = argparse.ArgumentParser() parser.add_argument('-popsize', type=int, default=50, help="Population size, default: 50.") parser.add_argument('-generations', type=int, default=5, help="Number of generations, default: 5.") parser.add_argument('-tournament', type=int, default=5, help="Tournament size, default: 5.") parser.add_argument('-pmut', type=float, default=0.7, help="Probability of mutation, default: 0.7") parser.add_argument('-pxov', type=float, default=0.2, help="Probability of crossover, default: 0.2") parser.add_argument('-hof_size', type=int, default=10, help="Number of genotypes in Hall of Fame. Default: 10.") parser.add_argument('-hof_savefile', type=str, required=False, help= 'If set, Hall of Fame will be saved in Framsticks file format (recommended extension *.gen. This also activates saving state (checpoint} file and auto-resuming from the saved state, if this file exists.') parser.add_argument('-save_only_best', type=bool, default=True, required=False, help="") parser.add_argument('-fitness_set_negative_to_zero', action='store_true', help="This flag forces fitness to become max(0,fitness), so it is always made non-negative. Using niching or novelty techniques without this flag (thus allowing negative fitness values) requires verifying/improving fitness diversification formulas to work as intended for both positive and negative fitness values.") return parser