def main(seed, exp_no, run_specs, test_set):
# set the randomized seed for experimentation duplication
random.seed(seed)
# set experiment number
experiment = exp_no
# assign the test set for GP
test_set = test_set
# sets the specs for the run
POP = run_specs['pop_size']
GENS = run_specs['gens']
HOF_SIZE = run_specs['hof_size']
CROSS_PROB = run_specs['cross_prob']
MUT_PROB = 1.0
IND_MUT_PROB = run_specs['mut_rate']
SELECTOR = run_specs['selector']
ELITISM = run_specs['elitism']
alg = run_specs['algorithm']
REC_FITS = run_specs['rec_fits']
SEEDED = run_specs['seeded_pop']
# sets hof and fs on/off
FS = run_specs['FS']
HOF = run_specs['HOF']
"""____SET UP ANY LISTS FOR TRACKING DATA HERE___"""
stats_rel_b = []
stats_bgp_b = []
stats_agp_b = []
stats_rel_r = []
stats_bgp_r = []
stats_agp_r = []
# set evolutionary operators
toolbox.register("mutate",
helpers.gaussian,
mu=0,
sigma=1.0,
indpb=IND_MUT_PROB)
toolbox.register("mate", tools.cxOnePoint)
toolbox.register("evaluate", helpers.evaluate)
if SELECTOR == 'SUS':
toolbox.register("select", helpers.selStochasticUniversalSampling)
elif SELECTOR == 'RANKED':
toolbox.register("select", helpers.selRanked)
# create gene templates
toolbox.register("turn_range", Gene, min_turn_range, max_turn_range)
toolbox.register("turn_angle", Gene, min_turn_angle, max_turn_angle)
toolbox.register("desired_displ", Gene, min_displ, max_displ)
toolbox.register("conv_range", Gene, min_conv_range, max_conv_range)
toolbox.register("no_closer_range", Gene, min_closer_range,
max_closer_range)
# create chromosome template
toolbox.register(
"tactic",
tools.initCycle,
creator.Tactic,
(toolbox.turn_range, toolbox.turn_angle, toolbox.desired_displ,
toolbox.conv_range, toolbox.no_closer_range),
n=CYCLES)
# create population template
toolbox.register("population", tools.initRepeat, list, toolbox.tactic, POP)
# create the population initializer
toolbox.register("population", tools.initRepeat, list, toolbox.tactic, POP)
"""<------------------------------------- START ---------------------------------------->"""
if SEEDED == 1:
print " no seeded pop"
else:
# initialize populations
blue_pop = toolbox.population()
red_pop = toolbox.population()
# initialize hall of fame
blue_hof = hof.HallOfFame(HOF_SIZE)
red_hof = hof.HallOfFame(HOF_SIZE)
# get working folder for this set of runs
folder_string = run_specs['folder']
# Make new folder for experiemnt results
save_path = os.getcwd()
sub_folder_string = folder_string + str(exp_no) + '_Seed_' + str(
seed) + '/'
folder = os.path.join(save_path, sub_folder_string)
if not os.path.isdir(folder):
os.makedirs(folder)
# create out file
text_file = sub_folder_string + 'results.txt'
sys.stdout = open(text_file, 'w')
# reset random seed to clock time
algseed = datetime.now()
random.seed(algseed)
algseed = random.randint(0, 10001)
random.seed(algseed)
print 'Algorithm seed: ', algseed
# create csv writer and file for fitness and initial/final population output
csv_fits = sub_folder_string + 'relative_fitness.csv'
fits = open(csv_fits, 'ab')
fit_writer = csv.writer(fits)
# csv writer for outputting all fitnesses of blue population
blue_pop_fits = sub_folder_string + 'blue_all_fits.csv'
blue_fits_file = open(blue_pop_fits, 'ab')
blue_fits_writer = csv.writer(blue_fits_file)
# do the same for red ^^^
red_pop_fits = sub_folder_string + 'red_all_fits.csv'
red_fits_file = open(red_pop_fits, 'ab')
red_fits_writer = csv.writer(red_fits_file)
blue_gp = sub_folder_string + 'blue_gp.csv'
blue_gp_file = open(blue_gp, 'ab')
blue_gp_writer = csv.writer(blue_gp_file)
blue_gp_writer.writerow(['best', 'avg'])
blue_chromosomes = sub_folder_string + 'blue_best.txt'
blue_best_file = open(blue_chromosomes, 'w')
red_gp = sub_folder_string + 'red_gp.csv'
red_gp_file = open(red_gp, 'ab')
red_gp_writer = csv.writer(red_gp_file)
red_gp_writer.writerow(['best', 'avg'])
red_chromosomes = sub_folder_string + 'red_best.txt'
red_best_file = open(red_chromosomes, 'w')
print "<-----INITIAL BLUE----->"
for b, ind in enumerate(blue_pop):
print "Chromosome ", str(b), ': '
print helpers.list_to_string(ind)
print "<-----INITIAL RED----->"
for r, ind in enumerate(red_pop):
print "Chromosome ", str(r), ': '
print helpers.list_to_string(ind)
# create csv writer and file for GP data
# csv_fits = sub_folder_string + 'Gen_performance.csv'
# gp = open(csv_fits, 'ab')
# gp_writer = csv.writer(gp)
# set up headings for graph csv
fit_writer.writerow(
('Generation', 'BestB', 'AvgB', 'WorstB', 'BestR', 'AvgR', 'WorstR'))
# set up GP headings
# gp_writer.writerow(('Generation', 'BestB_GP', 'AvgB_GP', 'BestR_GP', 'AvgR_GP'))
print(
'-------------------------------------Stats----------------------------------------'
)
print 'Seed: ', seed
print 'Selection: ', SELECTOR
print("Pop size: " + str(POP))
print("Generations: " + str(GENS))
print("Crossover Prob: " + str(CROSS_PROB))
print("Mutation Prob: " + str(IND_MUT_PROB))
print 'Elitism: ', ELITISM
print 'Hall of Fame: ', HOF
print 'Fitness Sharing: ', FS
print 'Algorithm: ', alg
i = 0
while i <= GENS:
i += 1
print('--------------------------------' + 'Generation: ' + str(i) +
'-----------------------------------')
""" ----------------------------------- EVALUATE THE POPULATIONS --------------------------------------- """
# Reset fitness scores to zero so they can be accumulated each generation
for index, x in enumerate(blue_pop):
x.fitness.values = (0.0, )
for index, y in enumerate(red_pop):
y.fitness.values = (0.0, )
"""<----------RELATIVE FITNESS EVALUATIONS--------->"""
# Evaluate the populations and return with summed fitness scores of all engagements
# THIS FITNESS SCORE NEEDS TO BE AVERAGED AFTER HOF EVALS
blue_pop, red_pop = helpers.sum_pop_v_pop_evals(blue_pop, red_pop)
"""<----------HALL OF FAME EVALUATIONS--------->"""
# check hof not empty, if not, loop through and evaluate
# Only if HOF is turned on
if HOF == 1:
if len(red_hof) != 0:
blue_pop = helpers.sum_pop_v_hof_evals(blue_pop, red_hof)
if len(blue_hof) != 0:
red_pop = helpers.sum_pop_v_hof_evals(red_pop, blue_hof)
"""<-------AVERAGE THE FITNESS SCORES------>"""
# average the accumulated fitness scores by size of population and also output fitnesses to csv
blue_fits_row = []
blue_fits_row.append(i)
for index, x in enumerate(blue_pop):
if HOF == 1:
x.fitness.values = (x.fitness.values[0] /
(POP + len(red_hof)), )
else:
x.fitness.values = (x.fitness.values[0] / POP, )
blue_fits_row.append(x.fitness.values[0])
red_fits_row = []
red_fits_row.append(i)
for index, y in enumerate(red_pop):
if HOF == 1:
y.fitness.values = (y.fitness.values[0] /
(POP + len(blue_hof)), )
else:
y.fitness.values = (y.fitness.values[0] / POP, )
red_fits_row.append(y.fitness.values[0])
if REC_FITS == 1:
blue_fits_writer.writerow(blue_fits_row)
red_fits_writer.writerow(red_fits_row)
"""------------GET BEST---------------------"""
blue_best_file.write("<------------ GEN " + str(i) +
"--------------->\n")
red_best_file.write("<------------ GEN " + str(i) +
"--------------->\n")
best_blue = tools.selBest(blue_pop, 5)
best_red = tools.selBest(red_pop, 5)
for index, b in enumerate(best_blue):
blue_best_file.write("Chromsome [" + str(index) + "]: " + "(" +
str(b.fitness.values[0]) + ")" +
helpers.list_to_string(b) + "\n")
for index, r in enumerate(best_red):
red_best_file.write("Chromsome [" + str(index) + "]: " + "(" +
str(r.fitness.values[0]) + ")" +
helpers.list_to_string(r) + "\n")
""" <---------------- Gen Perf ---------------->"""
# cloned so the GP can be calculated without wiping relative scores
blue_copy = list(map(toolbox.clone, best_blue))
red_copy = list(map(toolbox.clone, best_red))
# Reset fitness scores to zero so they can be accumulated each generation
for index, x in enumerate(blue_copy):
x.fitness.values = (0.0, )
for index, y in enumerate(red_copy):
y.fitness.values = (0.0, )
blue_copy = helpers.sum_pop_v_hof_evals(blue_copy, test_set)
red_copy = helpers.sum_pop_v_hof_evals(red_copy, test_set)
sum_gp_b = 0.0
sum_gp_r = 0.0
for index_x, x in enumerate(blue_copy):
x.fitness.values = (x.fitness.values[0] / len(test_set), )
sum_gp_b += x.fitness.values[0]
for index_y, y in enumerate(red_copy):
y.fitness.values = (y.fitness.values[0] / len(test_set), )
sum_gp_r += y.fitness.values[0]
avg_gp_b = sum_gp_b / len(blue_copy)
avg_gp_r = sum_gp_r / len(red_copy)
best_gp_b = tools.selBest(blue_copy, 1)[0].fitness.values[0]
best_gp_r = tools.selBest(red_copy, 1)[0].fitness.values[0]
blue_gp_writer.writerow([best_gp_b, avg_gp_b])
red_gp_writer.writerow([best_gp_r, avg_gp_r])
del blue_copy
del red_copy
"""<----------OUTPUT INFO TO TEXT FILE---------->"""
# BLUE
best_blue_ind = tools.selBest(blue_pop, 1)[0]
print 'Best Blue fitness score: ', best_blue_ind.fitness.values[0]
print 'Best blue chromosome: ', helpers.list_to_string(best_blue_ind)
worst_blue_ind = tools.selWorst(blue_pop, 1)[0]
print "Worst Blue fitness: ", worst_blue_ind.fitness.values[0]
print "Worst Blue chromosome: ", helpers.list_to_string(worst_blue_ind)
# get the average fitness of the generation
sum_fits = sum(ind.fitness.values[0] for ind in blue_pop)
avg_blue_fitness = sum_fits / POP
print "Generation average Blue fitness: ", avg_blue_fitness
# RED
# get best chromosome and output
best_red_ind = tools.selBest(red_pop, 1)[0]
print "Best Red fitness: ", best_red_ind.fitness.values[0]
print 'Best Red chromosome: ', helpers.list_to_string(best_red_ind)
# get worst and display
worst_red_ind = tools.selWorst(red_pop, 1)[0]
print "Worst Red fitness: ", worst_red_ind.fitness.values[0]
print "Worst Red chromosome: ", helpers.list_to_string(worst_red_ind)
# get the average fitness of the generation
sum_fits = sum(ind.fitness.values[0] for ind in red_pop)
avg_red_fitness = sum_fits / POP
print "Generation average red fitness: ", avg_red_fitness
# Write best avg & worst to fits_csv
fit_writer.writerow(
(i, best_blue_ind.fitness.values[0], avg_blue_fitness,
worst_blue_ind.fitness.values[0], best_red_ind.fitness.values[0],
avg_red_fitness, worst_red_ind.fitness.values[0]))
# if i % 5 == 0 or i == 1:
# # Fill the dictionary using the dict(key=value[, ...]) constructor
# cp = dict(population=blue_pop, generation=i, rndstate=random.getstate())
#
# with open(sub_folder_string + "zgeneration" + str(i) + ".pkl", "wb") as cp_file:
# cPickle.dump(cp, cp_file)
stats_rel_b.append(best_blue_ind.fitness.values[0])
stats_bgp_b.append(best_gp_b)
stats_agp_b.append(avg_gp_b)
stats_rel_r.append(best_red_ind.fitness.values[0])
stats_bgp_r.append(best_gp_r)
stats_agp_r.append(avg_gp_r)
if i > 10:
stats_rel_b.pop(0)
stats_bgp_b.pop(0)
stats_agp_b.pop(0)
stats_rel_r.pop(0)
stats_bgp_r.pop(0)
stats_agp_r.pop(0)
# if i > GENS-10:
# stats_rel_b.append(best_blue_ind.fitness.values[0])
# stats_bgp_b.append(best_gp_b)
# stats_agp_b.append(avg_gp_b)
# stats_rel_r.append(best_red_ind.fitness.values[0])
# stats_bgp_r.append(best_gp_r)
# stats_agp_r.append(avg_gp_r)
if i == GENS:
break
"""<------------HALL OF FAME UPDATE----------->"""
blue_hof.update(tools.selBest(blue_pop, 1), i)
red_hof.update(tools.selBest(red_pop, 1), i)
"""<--------------------BEGIN EVOLUTION------------------>"""
# Select offspring for next generation
if ELITISM == 1:
blue_offspring = toolbox.select(blue_pop, POP - 1, POP)
red_offspring = toolbox.select(red_pop, POP - 1, POP)
else:
blue_offspring = toolbox.select(blue_pop, POP, POP)
red_offspring = toolbox.select(red_pop, POP, POP)
# Clone the offspring so we can evolve
blue_offspring = list(map(toolbox.clone, blue_offspring))
red_offspring = list(map(toolbox.clone, red_offspring))
random.shuffle(blue_offspring)
random.shuffle(red_offspring)
# CROSSOVER
blue_cross_count = 0
# Blue
for child1, child2 in zip(blue_offspring[::2], blue_offspring[1::2]):
if random.random() < CROSS_PROB:
# MATE
toolbox.mate(child1, child2)
blue_cross_count += 1
# Red
red_cross_count = 0
for child1, child2 in zip(red_offspring[::2], red_offspring[1::2]):
if random.random() < CROSS_PROB:
# MATE
toolbox.mate(child1, child2)
red_cross_count += 1
print "blue crossover count: ", str(blue_cross_count)
print "red crossover count: ", str(red_cross_count)
# MUTATION
for mutant in blue_offspring:
if random.random() < MUT_PROB:
# MUTATE
for index, x in enumerate(mutant):
mutant[index].value = helpers.convert_range(
mutant[index].value, mutant[index].min,
mutant[index].max)
toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helpers.change_back(
mutant[index].value, mutant[index].min,
mutant[index].max)
helpers.bounds_check(mutant[index])
for mutant in red_offspring:
if random.random() < MUT_PROB:
# MUTATE
for index, x in enumerate(mutant):
mutant[index].value = helpers.convert_range(
mutant[index].value, mutant[index].min,
mutant[index].max)
toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helpers.change_back(
mutant[index].value, mutant[index].min,
mutant[index].max)
helpers.bounds_check(mutant[index])
if ELITISM == 1:
elite_blue = toolbox.clone(tools.selBest(blue_pop, 1))
elite_red = toolbox.clone(tools.selBest(red_pop, 1))
blue_offspring += elite_blue
red_offspring += elite_red
# REPLACE
blue_pop[:] = blue_offspring
red_pop[:] = red_offspring
print(
'-------------------------------------Hall Of Fame Regular----------------------------------------'
)
print "BLUE: "
# for chromosomes in blue_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(blue_hof) != 0:
blue_hof.print_hof()
print "RED: "
# for chromosomes in red_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(red_hof) != 0:
red_hof.print_hof()
print "Generation ", i, " complete."
print(
'-------------------------------------Hall Of Fame Regular----------------------------------------'
)
print "BLUE: "
# for chromosomes in blue_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(blue_hof) != 0:
blue_hof.print_hof()
print "RED: "
# for chromosomes in red_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(red_hof) != 0:
red_hof.print_hof()
print(
'-------------------------------------Stats----------------------------------------'
)
print 'Seed: ', seed
print 'Selection: ', SELECTOR
print("Pop size: " + str(POP))
print("Generations: " + str(GENS))
print("Crossover Prob: " + str(CROSS_PROB))
print("Mutation Prob: " + str(IND_MUT_PROB))
print 'Elitism: ', ELITISM
print 'Hall of Fame: ', HOF
print 'Fitness Sharing: ', FS
print 'Algorithm: ', alg
fits.close()
blue_fits_file.close()
red_fits_file.close()
red_best_file.close()
blue_best_file.close()
blue_gp_file.close()
red_gp_file.close()
stats = [
seed,
sum(stats_rel_b) / 10,
sum(stats_bgp_b) / 10,
sum(stats_agp_b) / 10,
sum(stats_rel_r) / 10,
sum(stats_bgp_r) / 10,
sum(stats_agp_r) / 10
]
# stats = {"blue_est_avg_gp": blue_est_avg_gp, "blue_est_best_gp": blue_est_best_gp,
# "red_est_avg_gp": red_est_avg_gp, "red_est_best_gp": red_est_best_gp}
return stats
def run_algorithm(self, seed):
# Set up hall of fame to keep track of the top chromosomes
# custom hall of fame to track the generation it was found
hall_of_fame = hof.HallOfFame(self.HOF_SIZE)
# standard hall of fame that comes with DEAP
hall_of_fame_with_dupes = hof.HallOfFameStandard(self.HOF_SIZE)
# write out all text to a file
file_name = str(seed) + '_with_HOF.txt'
sys.stdout = open(file_name, 'w')
print 'Seed:', seed
# track the current generation
i = 0
while i < self.GENERATIONS and not self.isConverged:
i += 1
print('--------------------------------' + 'Generation: ' + str(i) + '-----------------------------------')
# evaluate each chromosome in the population and assign its fitness score
for index, x in enumerate(self.population):
# update the chromosome, writes out to JSON tactics file
chromosome_parameters.update_ecu_basic_chromosome(x)
# use Ace0 to evaluate the chromosome
x.fitness.values = helper.multi_evaluate(self.num_of_processors)
# Select the best chromosome from this generation and display it
best_chromosome = tools.selBest(self.population, 10)[0]
print "Best chromosome is: ", best_chromosome.fitness.values
# check if the best chromosome value has changed
if float(best_chromosome.fitness.values[0]) == self.temp_best_fitness:
self.converge_tracker_2 += 1
if self.converge_tracker_2 >= self.converge_stagnant_fitness:
print 'CONVERGED due to stagnant fit'
self.isConverged = True
else:
self.converge_tracker_2 = 0
self.temp_best_fitness = best_chromosome.fitness.values[0]
# Get the over all fitness values
sum_fits = sum(ind.fitness.values[0] for ind in self.population)
average_fitness = sum_fits / self.POP
print 'Generation average fitness: ', average_fitness
csv_name = str(seed) + '_output.csv'
f = open(csv_name, 'ab')
writer = csv.writer(f)
writer.writerow((str(i), str(best_chromosome.fitness.values[0]), str(average_fitness)))
f.close()
# save best and average fitness to plot lists
self.plot_best_fitness.append(best_chromosome.fitness.values)
self.plot_average_fitness.append(average_fitness)
# Update the hall of fame to track the best chromosomes from each generation
hall_of_fame.update(self.population, i)
hall_of_fame_with_dupes.update(self.population)
print ('-----------------------------------Hall Of Fame at Gen ' + str(i) + '-----------------------------------')
hall_of_fame.print_hof()
# this is where we evolve the population
# Select the next generation individuals
offspring = self.toolbox.select(self.population, len(self.population))
# Clone the selected individuals so we can apply crossover
offspring = list(map(self.toolbox.clone, offspring))
# Apply crossover on the offspring
for child1, child2 in zip(offspring[::2], offspring[::-2]):
if random.random() < self.CROSSOVER_PROBABILITY:
# mate the two children
self.toolbox.mate(child1, child2)
# Apply mutation on the offspring
for mutant in offspring:
if random.random() < self.MUTATION_PROBABILITY:
for index, x in enumerate(mutant):
mutant[index].value = helper.convert_range(mutant[index].value, mutant[index].min,
mutant[index].max)
self.toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helper.change_back(mutant[index].value, mutant[index].min,
mutant[index].max)
# check if the mutated value is outside the min/max
helper.bounds_check(mutant[index])
# The population is entirely replaced by the offspring
self.population[:] = offspring
# check to see if the avg fitness is the same as the best fitness
if float(best_chromosome.fitness.values[0]) - average_fitness < 0.0001:
self.converge_tracker += 1
if self.converge_tracker >= self.converge_tracker_max:
print 'CONVERGED due to best = avg'
self.isConverged = True
else:
self.converge_tracker = 0
# elitism, carry over the best chromosome from this generation
self.population[0] = hall_of_fame[0]
# print the hall of fame to file so we can access the chromosomes
print ('-------------------------------------Hall Of Fame Regular----------------------------------------')
for chromosomes in hall_of_fame_with_dupes:
print 'Chromosome: ', helper.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness
print ('-------------------------------------Hall Of Fame with Gen----------------------------------------')
hall_of_fame.print_hof()
title = 'Seed: ' + str(seed)
# save the chart to an image file
visualization.draw_plot(title, self.plot_average_fitness, self.plot_best_fitness, 'average per generation',
'best fitness', 'average per generation', 'best fitness', 0, 250, 150, seed)
# delete the created objects so we can loop and automate the GA process for many seeds
del creator.fitness
del creator.Tactic
def main(seed, exp_no, run_specs, test_set):
# set the randomized seed for experimentation duplication
random.seed(seed)
# set experiment number
experiment = exp_no
# assign the test set for GP
test_set = test_set
# sets the specs for the run
POP = run_specs['pop_size']
GENS = run_specs['gens']
HOF_SIZE = run_specs['hof_size']
CROSS_PROB = run_specs['cross_prob']
MUT_PROB = 1.0
IND_MUT_PROB = run_specs['mut_rate']
SELECTOR = run_specs['selector']
ELITISM = run_specs['elitism']
alg = run_specs['algorithm']
REC_FITS = run_specs['rec_fits']
SEEDED = run_specs['seeded_pop']
# sets hof and fs on/off
FS = run_specs['FS']
HOF = run_specs['HOF']
"""____SET UP ANY LISTS FOR TRACKING DATA HERE___"""
plot_best_fitness = []
plot_average_fitness = []
plot_worst_fitness = []
blue_est_best_gp = []
red_est_best_gp = []
blue_est_avg_gp = []
red_est_avg_gp = []
# set evolutionary operators
toolbox.register("mutate",
helpers.gaussian,
mu=0,
sigma=0.2,
indpb=IND_MUT_PROB)
toolbox.register("mate", tools.cxOnePoint)
toolbox.register("evaluate", helpers.evaluate)
if SELECTOR == 'SUS':
toolbox.register("select", tools.selStochasticUniversalSampling)
elif SELECTOR == 'RANKED':
toolbox.register("select", helpers.selRanked)
# Initialise all the gene's to be used in the chromosome.
toolbox.register("px_min", Gene, MIN_DISTANCE, MAX_DISTANCE, True)
toolbox.register("px_max", Gene, MIN_DISTANCE, MAX_DISTANCE, False)
toolbox.register("py_min", Gene, MIN_DISTANCE, MAX_DISTANCE, True)
toolbox.register("py_max", Gene, MIN_DISTANCE, MAX_DISTANCE, False)
toolbox.register("pz_min", Gene, MIN_DISTANCE, MAX_DISTANCE, True)
toolbox.register("pz_max", Gene, MIN_DISTANCE, MAX_DISTANCE, False)
toolbox.register("vx_min", Gene, MIN_VELOCITY, MAX_VELOCITY, True)
toolbox.register("vx_max", Gene, MIN_VELOCITY, MAX_VELOCITY, False)
toolbox.register("vy_min", Gene, MIN_VELOCITY, MAX_VELOCITY, True)
toolbox.register("vy_max", Gene, MIN_VELOCITY, MAX_VELOCITY, False)
toolbox.register("vz_min", Gene, MIN_VELOCITY, MAX_VELOCITY, True)
toolbox.register("vz_max", Gene, MIN_VELOCITY, MAX_VELOCITY, False)
toolbox.register("ax_min", Gene, MIN_ACCEL, MAX_ACCEL, True)
toolbox.register("ax_max", Gene, MIN_ACCEL, MAX_ACCEL, False)
toolbox.register("ay_min", Gene, MIN_ACCEL, MAX_ACCEL, True)
toolbox.register("ay_max", Gene, MIN_ACCEL, MAX_ACCEL, False)
toolbox.register("az_min", Gene, MIN_ACCEL, MAX_ACCEL, True)
toolbox.register("az_max", Gene, MIN_ACCEL, MAX_ACCEL, False)
# Initialize the structure of the chromosome
# Sets tactic as a Tactic object and populates it with genes from gene template
toolbox.register(
"tactic",
tools.initCycle,
creator.Tactic,
(toolbox.px_min, toolbox.px_max, toolbox.py_min, toolbox.py_max,
toolbox.pz_min, toolbox.pz_max, toolbox.vx_min, toolbox.vx_max,
toolbox.vy_min, toolbox.vy_max, toolbox.vz_min, toolbox.vz_max,
toolbox.ax_min, toolbox.ax_max, toolbox.ay_min, toolbox.ay_max,
toolbox.az_min, toolbox.az_max),
n=CYCLES)
# create the population initializer
toolbox.register("population", tools.initRepeat, list, toolbox.tactic, POP)
"""<------------------------------------- START ---------------------------------------->"""
if SEEDED == 1:
blue_pop = pop_init.run()
red_pop = pop_init.run()
# Load the test set from the pickle file
# with open("biased_blue_pop.pkl", "r") as cp_file:
# cp = cPickle.load(cp_file)
# blue_pop = cp["population"]
# with open("biased_red_pop.pkl", "r") as cp_file:
# cp = cPickle.load(cp_file)
# red_pop = cp["population"]
else:
# initialize populations
blue_pop = toolbox.population()
red_pop = toolbox.population()
# initialize hall of fame
blue_hof = hof.HallOfFame(HOF_SIZE)
red_hof = hof.HallOfFame(HOF_SIZE)
# reset random seed to clock time
algseed = datetime.now()
random.seed(algseed)
algseed = random.randint(0, 1001)
random.seed(algseed)
print 'Algorithm seed: ', algseed
# get working folder for this set of runs
folder_string = run_specs['folder']
# Make new folder for experiemnt results
save_path = os.getcwd()
sub_folder_string = folder_string + str(exp_no) + '_Seed_' + str(
seed) + '/'
folder = os.path.join(save_path, sub_folder_string)
if not os.path.isdir(folder):
os.makedirs(folder)
# create out file
text_file = sub_folder_string + 'results.txt'
sys.stdout = open(text_file, 'w')
# create csv writer and file for fitness and initial/final population output
csv_fits = sub_folder_string + 'relative_fitness.csv'
fits = open(csv_fits, 'ab')
fit_writer = csv.writer(fits)
# create csv writer and file for detailed results for graph output
# csv_pops = sub_folder_string + 'populations.csv'
# pops = open(csv_pops, 'ab')
# pops_writer = csv.writer(pops)
# csv writer for outputting all fitnesses of blue population
blue_pop_fits = sub_folder_string + 'blue_all_fits.csv'
blue_fits_file = open(blue_pop_fits, 'ab')
blue_fits_writer = csv.writer(blue_fits_file)
# do the same for red ^^^
red_pop_fits = sub_folder_string + 'red_all_fits.csv'
red_fits_file = open(red_pop_fits, 'ab')
red_fits_writer = csv.writer(red_fits_file)
# create csv writer and file for GP data
csv_fits = sub_folder_string + 'Gen_performance.csv'
gp = open(csv_fits, 'ab')
gp_writer = csv.writer(gp)
# set up headings for graph csv
fit_writer.writerow(
('Generation', 'BestB', 'AvgB', 'WorstB', 'BestR', 'AvgR', 'WorstR'))
# set up GP headings
gp_writer.writerow(
('Generation', 'BestB_GP', 'AvgB_GP', 'BestR_GP', 'AvgR_GP'))
print 'Seed: ', seed
i = 0
while i <= GENS:
i += 1
print('--------------------------------' + 'Generation: ' + str(i) +
'-----------------------------------')
""" ----------------------------------- EVALUATE THE POPULATIONS --------------------------------------- """
# Reset fitness scores to zero so they can be accumulated each generation
for index, x in enumerate(blue_pop):
x.fitness.values = (0.0, )
for index, y in enumerate(red_pop):
y.fitness.values = (0.0, )
"""<----------RELATIVE FITNESS EVALUATIONS--------->"""
# Evaluate the populations and return with summed fitness scores of all engagements
# THIS FITNESS SCORE NEEDS TO BE AVERAGED AFTER HOF EVALS
blue_pop, red_pop = helpers.sum_pop_v_pop_evals(blue_pop, red_pop)
"""<----------HALL OF FAME EVALUATIONS--------->"""
# check hof not empty, if not, loop through and evaluate
# Only if HOF is turned on
if HOF == 1:
if len(red_hof) != 0:
blue_pop = helpers.sum_pop_v_hof_evals(blue_pop, red_hof)
if len(blue_hof) != 0:
red_pop = helpers.sum_pop_v_hof_evals(red_pop, blue_hof)
"""<-------AVERAGE THE FITNESS SCORES------>"""
# average the accumulated fitness scores by size of population and also output fitnesses to csv
blue_fits_row = []
blue_fits_row.append(i)
for index, x in enumerate(blue_pop):
if HOF == 1:
x.fitness.values = (x.fitness.values[0] /
(POP + len(red_hof)), )
else:
x.fitness.values = (x.fitness.values[0] / POP, )
blue_fits_row.append(x.fitness.values[0])
red_fits_row = []
red_fits_row.append(i)
for index, y in enumerate(red_pop):
if HOF == 1:
y.fitness.values = (y.fitness.values[0] /
(POP + len(blue_hof)), )
else:
y.fitness.values = (y.fitness.values[0] / POP, )
red_fits_row.append(y.fitness.values[0])
if REC_FITS == 1:
blue_fits_writer.writerow(blue_fits_row)
red_fits_writer.writerow(red_fits_row)
"""------------Generalisation Performance---------------------"""
# EVERY 5TH GEN RECORD STATS
# if i % 5 == 0 or i == 1:
# # CLONE THE POPS SO GP CAN BE CALCULATED WITHOUT AFFECTING FITNESS SCORES
# blue_copy = list(map(toolbox.clone, blue_pop))
# red_copy = list(map(toolbox.clone, red_pop))
# blue_copy = tools.selBest(blue_copy, 5)
# red_copy = tools.selBest(red_copy, 5)
# # Get generalisation performance of two populations
# blue_copy = helpers.sum_pop_v_hof_evals(blue_copy, test_set)
# for index_x, x in enumerate(blue_copy):
# x.fitness.values = (x.fitness.values[0] / len(test_set),)
#
# red_copy = helpers.sum_pop_v_hof_evals(red_copy, test_set)
# for index_x, x in enumerate(red_copy):
# x.fitness.values = (x.fitness.values[0] / len(test_set),)
#
# # create variables for estimated best and average GPs
# nBest_blue = tools.selBest(blue_copy, 5)
# blue_ebgp = tools.selBest(nBest_blue, 1)
# blue_est_best_gp.append(blue_ebgp[0].fitness.values[0])
# blue_eagp_tally = []
# for index, x in enumerate(nBest_blue):
# blue_eagp_tally.append(x.fitness.values[0])
# blue_avg = sum(blue_eagp_tally) / len(nBest_blue)
# blue_est_avg_gp.append(blue_avg)
#
# nBest_red = tools.selBest(red_copy, 5)
# red_ebgp = tools.selBest(nBest_red, 1)
# red_est_best_gp.append(red_ebgp[0].fitness.values[0])
# red_eagp_tally = []
# for index, x in enumerate(nBest_red):
# red_eagp_tally.append(x.fitness.values[0])
# red_avg = sum(red_eagp_tally) / len(nBest_red)
# red_est_avg_gp.append(red_avg)
"""<----------OUTPUT INFO TO TEXT FILE---------->"""
# BLUE
best_blue_ind = tools.selBest(blue_pop, 1)[0]
print 'Best Blue fitness score: ', best_blue_ind.fitness.values[0]
print 'Best blue chromosome: ', helpers.list_to_string(best_blue_ind)
worst_blue_ind = tools.selWorst(blue_pop, 1)[0]
print "Worst Blue fitness: ", worst_blue_ind.fitness.values[0]
# get the average fitness of the generation
sum_fits = sum(ind.fitness.values[0] for ind in blue_pop)
avg_blue_fitness = sum_fits / POP
print "Generation average Blue fitness: ", avg_blue_fitness
# if i % 5 == 0 or i == 1:
# print 'Performance Measures:'
# # output the GP and diversity of the generation
# print 'Blue Estimated Average GP: ', blue_avg
# print 'Blue Estimated Best GP: ', blue_ebgp[0].fitness.values[0]
#
# #Print the 5 best GP inds
# print 'Top Blue GP performers'
# for index, x in enumerate(nBest_blue):
# print index+1, ':'
# print helpers.list_to_string(x), 'GP: ', x.fitness.values[0]
# RED
# get best chromosome and output
best_red_ind = tools.selBest(red_pop, 1)[0]
print "Best Red fitness: ", best_red_ind.fitness.values[0]
print 'Best Red chromosome: ', helpers.list_to_string(best_red_ind)
# get worst and display
worst_red_ind = tools.selWorst(red_pop, 1)[0]
print "Worst Red fitness: ", worst_red_ind.fitness.values[0]
# get the average fitness of the generation
sum_fits = sum(ind.fitness.values[0] for ind in red_pop)
avg_red_fitness = sum_fits / POP
print "Generation average red fitness: ", avg_red_fitness
# if i % 5 == 0 or i == 1:
# print 'Performance Measures:'
# # output the GP and diversity of the generation
# print 'Red Estimated Average GP: ', red_avg
# print 'Red Estimated Best GP: ', red_ebgp[0].fitness.values[0]
#
# # Print the 5 best GP inds
# print 'Top red GP performers'
# for index, x in enumerate(nBest_red):
# print index + 1, ':'
# print helpers.list_to_string(x), 'GP: ', x.fitness.values[0]
# Write best avg & worst to fits_csv
fit_writer.writerow(
(i, best_blue_ind.fitness.values[0], avg_blue_fitness,
worst_blue_ind.fitness.values[0], best_red_ind.fitness.values[0],
avg_red_fitness, worst_red_ind.fitness.values[0]))
# if i % 5 == 0 or i == 1:
# # save the GP values to the GP file
# gp_writer.writerow((i, blue_ebgp[0].fitness.values[0], blue_avg, red_ebgp[0].fitness.values[0], red_avg))
# if i % 5 == 0 or i == 1:
# # Fill the dictionary using the dict(key=value[, ...]) constructor
# cp = dict(population=blue_pop, generation=i, rndstate=random.getstate())
#
# with open(sub_folder_string + "zgeneration" + str(i) + ".pkl", "wb") as cp_file:
# cPickle.dump(cp, cp_file)
if i == GENS:
break
"""<------------HALL OF FAME UPDATE----------->"""
blue_hof.update(tools.selBest(blue_pop, 1), i)
red_hof.update(tools.selBest(red_pop, 1), i)
"""<------------FITNESS SHARING------------>"""
""" NEEDS TO BE FIXED IN HELPERS TO USE NEW CHROMOSOME """
#MUST BE LAST THING BEFORE EVOLUTION HAPPENS
# if FS == 1:
# blue_pop = helpers.share_fitness(blue_pop, 0.2)
# red_pop = helpers.share_fitness(red_pop, 0.2)
"""<--------------------BEGIN EVOLUTION------------------>"""
# Select offspring for next generation
if ELITISM == 1:
blue_offspring = toolbox.select(blue_pop, POP - 1)
red_offspring = toolbox.select(red_pop, POP - 1)
else:
blue_offspring = toolbox.select(blue_pop, POP)
red_offspring = toolbox.select(red_pop, POP)
# Clone the offspring so we can evolve
blue_offspring = list(map(toolbox.clone, blue_offspring))
red_offspring = list(map(toolbox.clone, red_offspring))
# CROSSOVER
# Blue
for child1, child2 in zip(blue_offspring[::2], blue_offspring[1::2]):
if random.random() < CROSS_PROB:
# MATE
toolbox.mate(child1, child2)
# Red
for child1, child2 in zip(red_offspring[::2], red_offspring[1::2]):
if random.random() < CROSS_PROB:
# MATE
toolbox.mate(child1, child2)
# MUTATION
for mutant in blue_offspring:
if random.random() < MUT_PROB:
# MUTATE
for index, x in enumerate(mutant):
mutant[index].value = helpers.convert_range(
mutant[index].value, mutant[index].min,
mutant[index].max)
toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helpers.change_back(
mutant[index].value, mutant[index].min,
mutant[index].max)
helpers.bounds_check(mutant[index])
for mutant in red_offspring:
if random.random() < MUT_PROB:
# MUTATE
for index, x in enumerate(mutant):
mutant[index].value = helpers.convert_range(
mutant[index].value, mutant[index].min,
mutant[index].max)
toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helpers.change_back(
mutant[index].value, mutant[index].min,
mutant[index].max)
helpers.bounds_check(mutant[index])
if ELITISM == 1:
blue_offspring += tools.selBest(blue_pop, 1)
red_offspring += tools.selBest(red_pop, 1)
# REPLACE
blue_pop[:] = blue_offspring
red_pop[:] = red_offspring
print(
'-------------------------------------Hall Of Fame Regular----------------------------------------'
)
print "BLUE: "
# for chromosomes in blue_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(blue_hof) != 0:
blue_hof.print_hof()
print "RED: "
# for chromosomes in red_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(red_hof) != 0:
red_hof.print_hof()
print "Generation ", i, " complete."
print(
'-------------------------------------Hall Of Fame Regular----------------------------------------'
)
print "BLUE: "
# for chromosomes in blue_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(blue_hof) != 0:
blue_hof.print_hof()
print "RED: "
# for chromosomes in red_hof:
# print 'Chromosome: ', helpers.list_to_string(chromosomes), 'Fitness: ', chromosomes.fitness.values
if len(red_hof) != 0:
red_hof.print_hof()
print(
'-------------------------------------Stats----------------------------------------'
)
print 'Seed: ', seed
print 'Selection: ', SELECTOR
print("Pop size: " + str(POP))
print("Generations: " + str(GENS))
print("Crossover Prob: " + str(CROSS_PROB))
print("Mutation Prob: " + str(IND_MUT_PROB))
print 'Elitism: ', ELITISM
print 'Hall of Fame: ', HOF
print 'Fitness Sharing: ', FS
print 'Algorithm: ', alg
# Print the 5 best GP inds
# print 'Top 5 Blue GP performers:'
# for index, x in enumerate(nBest_blue):
# print index + 1, ':'
# print helpers.list_to_string(x), 'GP: ', x.fitness.values[0]
# Print the 5 best GP inds
# print 'Top red GP performers:'
# for index, x in enumerate(nBest_red):
# print index + 1,":"
# print helpers.list_to_string(x), 'GP: ', x.fitness.values[0]
# pops_writer.writerow(('Final Blue population fitness scores',))
# for index, x in enumerate(blue_pop):
# pops_writer.writerow(x.fitness.values)
# pops_writer.writerow(('Final Red population fitness scores',))
# for index, y in enumerate(red_pop):
# pops_writer.writerow(y.fitness.values)
# blue_est_avg_gp = sum(blue_est_avg_gp) / len(blue_est_avg_gp)
# red_est_avg_gp = sum(red_est_avg_gp) / len(red_est_avg_gp)
#
# blue_est_best_gp = sum(blue_est_best_gp) / len(blue_est_best_gp)
# red_est_best_gp = sum(red_est_best_gp) / len(red_est_best_gp)
fits.close()
# pops.close()
blue_fits_file.close()
red_fits_file.close()
# stats = {"blue_est_avg_gp": blue_est_avg_gp, "blue_est_best_gp": blue_est_best_gp,
# "red_est_avg_gp": red_est_avg_gp, "red_est_best_gp": red_est_best_gp}
return #stats
def run_algorithm(self, seed):
# Set up custom hall of fame to keep track of the top chromosomes and their generations
hall_of_fame = hof.HallOfFame(self.HOF_SIZE)
# set up standard hall of fame that comes with DEAP
hall_of_fame_with_dupes = tools.HallOfFame(self.HOF_SIZE)
# Get date and time
date_time = datetime.datetime.now().strftime("%m-%d_%I%M%p")
# print out to file
file_name = date_time + '.txt'
sys.stdout = open(file_name, 'w')
print 'Seed:', seed
i = 0
while i < self.GENERATIONS and not self.isConverged:
i += 1
print('--------------------------------' + 'Generation: ' +
str(i) + '-----------------------------------')
# evaluate each chromosome in the population and assign its fitness score
for index, x in enumerate(self.population):
# update the chromosome, write out to JSON tactical file
chromosome_parameters.update_chromosome(
x[0].value, x[1].value, x[2].value, x[3].value, x[4].value)
# use Ace0 to evaluate the chromosome
x.fitness.values = helper.evaluate(self.repetitions)
# Select the best chromosome from this generation and display it
best_chromosome = tools.selBest(self.population, 1)[0]
print "Best chromosome is: ", helper.list_to_string(
best_chromosome), best_chromosome.fitness.values
# Select worst chromosome and display
worst_chromosome = tools.selWorst(self.population, 1)[0]
print "Worst chromosome is: ", helper.list_to_string(
worst_chromosome), worst_chromosome.fitness.values
# Get the over all fitness values
sum_fits = sum(ind.fitness.values[0] for ind in self.population)
average_fitness = sum_fits / self.POP
print 'Generation average fitness: ', average_fitness
# save best and average fitness to plot lists
self.plot_best_fitness.append(best_chromosome.fitness.values)
self.plot_average_fitness.append(average_fitness)
self.plot_worst_fitness.append(worst_chromosome.fitness.values)
# Update the hall of fame to track the best chromosomes from each generation
hall_of_fame.update(self.population, i)
hall_of_fame_with_dupes.update(self.population)
# hall_of_fame.print_hof()
# this is where we evolve the population
# Select the next generation individuals
offspring = self.toolbox.select(self.population,
len(self.population))
# Clone the selected individuals so we can apply crossover
offspring = list(map(self.toolbox.clone, offspring))
# Apply crossover on the offspring
for child1, child2 in zip(offspring[::2], offspring[::-2]):
if random.random() < self.CROSSOVER_PROBABILITY:
# mate the two children
self.toolbox.mate(child1, child2)
# Apply mutation on the offspring
for mutant in offspring:
if random.random() < self.MUTATION_PROBABILITY:
# print 'Mutated Chromosome before: ', helper.list_to_string(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helper.convert_range(
mutant[index].value, mutant[index].min,
mutant[index].max)
self.toolbox.mutate(mutant)
for index, x in enumerate(mutant):
mutant[index].value = helper.change_back(
mutant[index].value, mutant[index].min,
mutant[index].max)
helper.bounds_check(mutant[index])
# print 'Mutated Chromosome after: ', helper.list_to_string(mutant)
# The population is entirely replaced by the offspring
self.population[:] = offspring
if float(best_chromosome.fitness.values[0]
) - average_fitness < 0.0001:
self.converge_tracker += 1
if self.converge_tracker >= self.converge_tracker_max:
print 'CONVERGED'
self.isConverged = True
else:
self.converge_tracker = 0
# # Elitism
self.population[0] = hall_of_fame_with_dupes[0]
print(
'-------------------------------------Hall Of Fame Regular----------------------------------------'
)
for chromosomes in hall_of_fame_with_dupes:
print 'Chromosome: ', helper.list_to_string(
chromosomes), 'Fitness: ', chromosomes.fitness
print(
'-------------------------------------Hall Of Fame with Gen----------------------------------------'
)
hall_of_fame.print_hof()
print(
'-------------------------------------Stats----------------------------------------'
)
print("Pop size: " + str(self.POP))
print("Generations: " + str(self.GENERATIONS))
print("Crossover Prob: " + str(self.CROSSOVER_PROBABILITY))
print("Mutation Prob: " + str(self.MUTATION_PROBABILITY))
# Select the best chromosome from this generation and display it
best_chromosome = tools.selBest(self.population, 1)[0]
print "Best chromosome is: ", helper.list_to_string(
best_chromosome), best_chromosome.fitness.values
# Select worst chromosome and display
worst_chromosome = tools.selWorst(self.population, 1)[0]
print "Worst chromosome is: ", helper.list_to_string(
worst_chromosome), worst_chromosome.fitness.values
# Get the over all fitness values
sum_fits = sum(ind.fitness.values[0] for ind in self.population)
average_fitness = sum_fits / self.POP
print 'Generation average fitness: ', average_fitness
title = 'Seed: ' + str(seed)
visualization.draw_plot(title, self.plot_average_fitness,
self.plot_best_fitness,
self.plot_worst_fitness,
'average per generation', 'best fitness',
'worst fitness', 'generation', 'fitness', 1,
250, 150, date_time)
del creator.fitness
del creator.Tactic