def test_nsga2():
NDIM = 5
BOUND_LOW, BOUND_UP = 0.0, 1.0
MU = 16
NGEN = 100
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, BOUND_LOW, BOUND_UP)
toolbox.register("individual", tools.initRepeat,
creator.__dict__[INDCLSNAME], toolbox.attr_float, NDIM)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.register("mate",
tools.cxSimulatedBinaryBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0)
toolbox.register("mutate",
tools.mutPolynomialBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0,
indpb=1.0 / NDIM)
toolbox.register("select", tools.selNSGA2)
pop = toolbox.population(n=MU)
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop, len(pop))
for gen in range(1, NGEN):
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= 0.9:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop + offspring, MU)
hv = hypervolume(pop, [11.0, 11.0])
# hv = 120.777 # Optimal value
assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (
hv, HV_THRESHOLD)
for ind in pop:
assert not (any(numpy.asarray(ind) < BOUND_LOW)
or any(numpy.asarray(ind) > BOUND_UP))
def main(seed=1):
random.seed(seed)
NGEN = 100
MU = 100
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population()
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, [x for x in invalid_ind])
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# print("Indiv num:{} fit: {}\n".format(ind, fit))
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
# print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
return pop, logbook
def nsgaii(model, mu, ngen, cxpb, mutpb):
assert mu % 4 == 0, "Error: mu is not divisible by 4"
logging.debug("Working on model " + model.name + " @ NSGAII.py::nsgaii")
toolbox = base.Toolbox()
toolbox.register('mate', tools.cxOnePoint)
toolbox.register('mutate',
tools.mutPolynomialBounded,
low=0,
up=1.0,
eta=20.0,
indpb=1.0 / model.decNum)
toolbox.register('select', tools.selNSGA2)
pop_df = model.init_random_pop(mu)
model.eval_pd_df(pop_df, normalized=True)
pop = model.pd_to_deap(pop_df)
pop = toolbox.select(pop, len(pop))
for gen in range(1, ngen):
# _show_pop(sorted(pop, key=lambda i: i[0]))
logging.debug("Running at gen " + str(gen))
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
t_offspring = list()
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cxpb:
toolbox.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
if random.random() <= mutpb:
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values
del ind2.fitness.values
# saving new configurations
if not ind1.fitness.valid:
t_offspring.append(ind1)
if not ind1.fitness.valid:
t_offspring.append(ind2)
# eval
offspring_df = model.deap_to_pd(t_offspring)
model.eval_pd_df(offspring_df, normalized=True)
offspring = model.pd_to_deap(offspring_df)
pop = toolbox.select(pop + offspring, mu)
toolbox.unregister('mate')
toolbox.unregister('mutate')
toolbox.unregister('select')
res = sortLogNondominated(pop, k=10,
first_front_only=True) # k value is non-sense
return model.deap_to_pd(res)
def main(seed=None):
random.seed(seed)
NGEN = 250
MU = 100
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", tools.mean)
stats.register("std", tools.std)
stats.register("min", min)
stats.register("max", max)
column_names = ["gen", "evals"]
column_names.extend(stats.functions.keys())
logger = tools.EvolutionLogger(column_names)
logger.logHeader()
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
stats.update(pop)
logger.logGeneration(evals=len(invalid_ind), gen=0, stats=stats)
# Begin the generational process
for gen in range(1, NGEN):
# Variate the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
stats.update(pop)
logger.logGeneration(evals=len(invalid_ind), gen=gen, stats=stats)
return pop
def main(seed=None):
random.seed(seed)
NGEN = 250
MU = 100
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
return pop, logbook
def select_parents(pop, toolbox, n_selected, algorithm):
if algorithm == 'nsga2' or algorithm == 'rmota' or algorithm == 'mmota':
offspring = tools.selTournamentDCD(pop, len(pop))
elif algorithm == 'dtas':
offspring = toolbox.select(pop, n_selected)
else:
raise exceptions.UnrecognizedAlgorithmException
return offspring
def action(model, mu, ngen, cxpb, mutpb):
toolbox = model.toolbox
toolbox.register('mate', tools.cxOnePoint)
toolbox.register('mutate',
tools.mutPolynomialBounded,
low=0,
up=1.0,
eta=20.0,
indpb=1.0 / model.decsNum)
toolbox.register('select', tools.selNSGA2)
stats = tools.Statistics(lambda ind: ind)
# PF0 = list()
# with open(
# os.path.dirname(os.path.abspath(__file__)) + '/../Metrics/PF_0/' +
# model.name + '.txt', 'r') as f:
# for l in f:
# e = l.strip('\n').split(' ')
# e = [float(i) for i in e]
# PF0.append(e)
# stats.register("gd_gs_pfs_hv", self_stats, model=model, PF0=PF0)
logbook = tools.Logbook()
# logbook.header = "gen", "gd_gs_pfs_hv"
pop = random_pop(model, mu)
for p in pop:
model.eval(p)
pop = toolbox.select(pop, len(pop))
t = time.time()
for gen in range(1, ngen):
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [copy.deepcopy(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cxpb:
toolbox.mate(ind1, ind2)
if random.random() <= mutpb:
toolbox.mutate(ind1)
toolbox.mutate(ind2)
model.eval(ind1)
model.eval(ind2)
pop = toolbox.select(pop + offspring, mu)
# print(gen)
record = stats.compile(pop)
logbook.record(gen=gen, **record)
print(logbook.stream)
print(time.time() - t)
for p in pop:
model.eval(p, normalized=False)
return pop
def main(seed=None):
random.seed(seed)
NGEN = ngen
MU = npop
CXPB = p_cross
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "min", "max"
pop = toolbox.population(n=MU)
pop_ini = pop[:]
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
if PRINT:
print(logbook.stream)
for gen in range(1, NGEN):
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
if PRINT:
print(logbook.stream)
# print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
return pop, pop_ini, logbook
def main():
temp = Chromosome(0)
creator.create("FitnessMin", base.Fitness, weights=(-1.0, ))
creator.create("Individual", list, fitness=creator.FitnessMin)
MU = 20
NGEN = 100
CXPB = 0.95
toolbox = base.Toolbox()
toolbox.register("individual", initialisation, creator.Individual)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", calculateFitness)
toolbox.register("select", tools.selNSGA2)
toolbox.register("mate", tools.cxOnePoint)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
pop = toolbox.population(n=MU)
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
plotter = []
for i in range(NGEN):
pop = toolbox.select(pop, len(pop))
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
plotter.append(mean([k.fitness.values[0] for k in pop]))
plt.plot(plotter)
plt.xlabel('Number of iterations')
plt.ylabel('Fitness of the population')
plt.show()
def multiobjective(pop, toolbox, ngen, cxpb, stats, halloffame):
"""
This code is based of the NSGA-II example from:
https://github.com/DEAP/deap/blob/master/examples/ga/nsga2.py
"""
logbook = tools.Logbook()
logbook.header = 'gen', 'evals', 'std', 'min', 'avg', 'max'
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
halloffame.update(pop)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cxpb:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, len(pop))
halloffame.update(pop)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
return pop, logbook
def run(self):
self.setup()
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in self.pop if not ind.fitness.valid]
fitnesses = list((self.toolbox.map(self.toolbox.evaluate,
invalid_ind)))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
self.pop = self.toolbox.select(self.pop, len(self.pop))
record = self.stats.compile(self.pop)
self.logbook.record(gen=0, evals=len(invalid_ind), **record)
print(self.logbook.stream)
# Begin the generational process
for i_generation in range(1, self.n_generation):
# Vary the population
offspring = tools.selTournamentDCD(self.pop, len(self.pop))
offspring = [self.toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= self.crossover_probability:
self.toolbox.mate(ind1, ind2)
self.toolbox.mutate(ind1)
self.toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
self.pop = self.toolbox.select(self.pop + offspring,
self.n_population)
record = self.stats.compile(self.pop)
self.logbook.record(gen=i_generation,
evals=len(invalid_ind),
**record)
print(self.logbook.stream)
print("Final population hypervolume is %f" %
hypervolume(self.pop, [11.0, 11.0]))
return self.pop, self.logbook
def EMO(gen):
INDIV = 100
CXPB = 0.9
NGEN = gen
stats = tools.Statistics(lambda ind: ind.fitness.values)
pop = toolbox.population(n=INDIV)
# applying fitness to people with invalid fitnesses
invalid_individual = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_individual)
for ind, fit in zip(invalid_individual, fitnesses):
ind.fitness.values = fit
# assign crowding distance
pop = toolbox.select(pop, len(pop))
# go through generations
for gen in range(1, NGEN):
# selection using dominance
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
# applying crossover
for indiv1, indiv2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(indiv1, indiv2)
#mutates based on indp prob (1/dimensions)
toolbox.mutate(indiv1)
toolbox.mutate(indiv2)
del indiv1.fitness.values, indiv2.fitness.values
# evaluating fitness of individuals with invalid fitnesses
invalid_individual = [
ind for ind in offspring if not ind.fitness.valid
]
fitnesses = toolbox.map(toolbox.evaluate, invalid_individual)
for ind, fit in zip(invalid_individual, fitnesses):
ind.fitness.values = fit
# Chossing a population for the next generation
pop = toolbox.select(pop + offspring, INDIV)
return pop
def test_nsga2():
NDIM = 5
BOUND_LOW, BOUND_UP = 0.0, 1.0
MU = 16
NGEN = 100
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, BOUND_LOW, BOUND_UP)
toolbox.register("individual", tools.initRepeat, creator.__dict__[INDCLSNAME], toolbox.attr_float, NDIM)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0)
toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0, indpb=1.0/NDIM)
toolbox.register("select", tools.selNSGA2)
pop = toolbox.population(n=MU)
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop, len(pop))
for gen in range(1, NGEN):
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= 0.9:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop + offspring, MU)
hv = hypervolume(pop, [11.0, 11.0])
# hv = 120.777 # Optimal value
assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (hv, HV_THRESHOLD)
def runGenerations(self):
# Running algorithm for given number of generations
for gen in range(self.num_gen):
print(f"{20*'#'} Currently Evaluating {gen} Generation {20*'#'}")
# Selecting individuals
# Selecting offsprings from the population, about 1/2 of them
self.offspring = tools.selTournamentDCD(self.pop, len(self.pop))
self.offspring = [
self.toolbox.clone(ind) for ind in self.offspring
]
# Performing , crossover and mutation operations according to their probabilities
for ind1, ind2 in zip(self.offspring[::2], self.offspring[1::2]):
# Mating will happen 80% of time if cross_prob is 0.8
if random.random() <= self.cross_prob:
# print("Mating happened")
self.toolbox.mate(ind1, ind2)
# If cross over happened to the individuals then we are deleting those individual
# fitness values, This operations are being done on the offspring population.
del ind1.fitness.values, ind2.fitness.values
self.toolbox.mutate(ind1)
self.toolbox.mutate(ind2)
# Calculating fitness for all the invalid individuals in offspring
self.invalid_ind = [
ind for ind in self.offspring if not ind.fitness.valid
]
self.fitnesses = self.toolbox.map(self.toolbox.evaluate,
self.invalid_ind)
for ind, fit in zip(self.invalid_ind, self.fitnesses):
ind.fitness.values = fit
# Recalcuate the population with newly added offsprings and parents
# We are using NSGA2 selection method, We have to select same population size
self.pop = self.toolbox.select(self.pop + self.offspring,
self.pop_size)
# Recording stats in this generation
recordStat(self.invalid_ind, self.logbook, self.pop, self.stats,
gen + 1)
print(f"{20 * '#'} End of Generations {20 * '#'} ")
def NSGA2(name):
model = DimacsModel(name)
toolbox = model.toolbox
toolbox.register('mutate', model.bit_flip_mutate)
toolbox.register('select', tools.selNSGA2)
toolbox.register('mate', model.cxTwoPoint)
# get the initial generation from the result of sway
# and evaluate them
pop = load_sway_results(model)
toolbox.map(model.eval_ind, pop)
# the parameters for NSGA-II as follows
MU = 100
NGEN = 100
CXPB = 0.9
# start the NSGA2 algorithms
for _ in range(100-len(pop)):
pop.append(random.choice(pop))
random.shuffle(pop)
for gen in range(1, NGEN):
# vary the population
tools.emo.assignCrowdingDist(pop)
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
map(model.eval, offspring)
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
valid_pop = [i for i in pop if i.fitness.values[0] <=0]
hv, spread, _, size, _ = stat_basing_on_pop(valid_pop, False)
print(hv, spread, size)
return hv, spread, size
def selTournamentDCD(problem, individuals):
# Evaluate any new guys
for individual in individuals:
if not individual.valid:
individual.evaluate()
# Format a population data structure usable by DEAP's package
dIndividuals = deap_format(problem, individuals)
# Assign crowding distance
tools.emo.assignCrowdingDist(dIndividuals)
# Select elites
selectees = tools.selTournamentDCD(dIndividuals, len(individuals))
# Update beginning population data structure
selectedIndices = [i for i,sel in enumerate(selectees)]
return [individuals[s] for s in selectedIndices], len(individuals)
def selTournamentDCD(problem, individuals):
# Evaluate any new guys
for individual in individuals:
if not individual.valid:
individual.evaluate()
# Format a population data structure usable by DEAP's package
dIndividuals = deap_format(problem, individuals)
# Assign crowding distance
tools.emo.assignCrowdingDist(dIndividuals)
# Select elites
selectees = tools.selTournamentDCD(dIndividuals, len(individuals))
# Update beginning population data structure
selectedIndices = [i for i, sel in enumerate(selectees)]
return [individuals[s] for s in selectedIndices], len(individuals)
def ZDT1_Selection(self, pop):
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
# # Select the next generation individuals
# offspring = toolbox.select(pop, len(pop))
# # Clone the selected individuals
# offspring = list(map(toolbox.clone, offspring))
# Apply crossover and mutation on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]): #get every single pair
if random.random() < self.CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < self.MUTPB:
#print mutant
toolbox.mutate(mutant)
#print mutant
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
# print(" Cross or Mutation: %s" % len(invalid_ind))
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
off = toolbox.select(pop + offspring, self.num_population)
pop[:] = off
def varCustom(population, toolbox, lambda_, cxpb=CXPB): offspring = [] for _ in xrange(lambda_): op_choice = random.random() if op_choice < cxpb: # produce offspring via crossover if method == 'NSGA2': parents = map(toolbox.clone, tools.selTournamentDCD(population, 2)) elif method == 'SPEA2': parents = map(toolbox.clone, tools.selTournamentSPEA2(population, 2)) ind1, ind2 = parents[0], parents[1] ind1, ind2 = toolbox.mate(ind1, ind2) del ind1.fitness.values offspring.append(ind1) else: ind1 = toolbox.clone(random.choice(population)) #ind1, = toolbox.mutate(ind1) del ind1.fitness.values offspring.append(ind1) for i in range(len(offspring)): # produce offspring via mutation offspring[i], = toolbox.mutate(offspring[i]) return offspring
def main(seed=None):
random.seed(seed)
NGEN = 250
MU = 100
log_interval = 25
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
stats.register("median", np.median, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max", "median"
pop = toolbox.population(n=MU)
hof = tools.ParetoFront()
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
hof.update(pop)
basepath = os.path.dirname(os.path.abspath(__file__))
log_dir = '{}/logs/{}/'.format(basepath, time.strftime('%y%m%d-%H%M%S'))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# log initial population
os.makedirs('{}0'.format(log_dir))
for i, agent in enumerate(pop):
agent[0].save_weights('{}0/{}_weights.csv'.format(log_dir, i), overwrite=True)
log_population(pop, '{}0'.format(log_dir))
# Begin the generational process
for gen in range(1, NGEN+1):
# Get Offspring
# first get pareto front
pareto_fronts = tools.sortNondominated(pop, len(pop))
selection = pareto_fronts[0]
len_pareto = len(pareto_fronts[0])
rest = list(chain(*pareto_fronts[1:]))
if len(rest) % 4:
rest.extend(random.sample(selection, 4 - (len(rest) % 4)))
selection.extend(tools.selTournamentDCD(rest, len(rest)))
offspring = [toolbox.mutate(toolbox.clone(ind)) for ind in selection[:len(pop)]]
# Revaluate the individuals in last population
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
# Evaluate the new offspring
fitnesses = toolbox.map(toolbox.evaluate, offspring)
for ind, fit in zip(offspring, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
hof.update(offspring)
plot_population(pop, offspring, lim = [[10,120],[0,0],[0,4]])
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
pareto_fronts = tools.sortNondominated(pop, len(pop))
plot_selection(pop, pareto_front_size=len(pareto_fronts[0]), lim = [[10,120],[0,0],[0,4]])
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(offspring)+len(pop), **record)
print(logbook.stream)
if gen % log_interval == 0 or gen == NGEN:
os.makedirs('{}{}'.format(log_dir, gen))
for i, agent in enumerate(pop):
agent[0].save_weights('{}{}/{}_weights.csv'.format(log_dir, gen, i), overwrite=True)
log_population(pop, '{}{}'.format(log_dir, gen))
with open('{}/gen_stats.txt'.format(log_dir), 'w') as fp:
np.savetxt(fp, logbook, fmt="%s")
plot_population(pop)
print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0, 11.0]))
os.makedirs('{}hof'.format(log_dir))
for i, agent in enumerate(hof):
agent[0].save_weights('{}hof/{}_weights.csv'.format(log_dir, i), overwrite=True)
log_population(hof, '{}hof'.format(log_dir))
return pop, logbook
def search(toolbox, seed=None, gens=500, mu=200, verbose=False):
random.seed(seed)
CXPB = 0.9 # pravdepodobnost krizenia?
stats = tools.Statistics(lambda ind: ind.fitness.values)
# stats.register("avg", numpy.mean, axis=0)
# stats.register("std", numpy.std, axis=0)
# stats.register("min", np.min, axis=0)
# stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "hypervolume"
# vytvorime inicialnu populaciu
pop = toolbox.population(n=mu)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Choose worst solution as reference point for hypervolume calculation
ref = np.max([x.fitness.values for x in pop], axis=0) + 1
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
best_hypervolume = hypervolume(pop, ref)
record['hypervolume'] = best_hypervolume
logbook.record(gen=0, evals=len(invalid_ind), **record)
output(logbook.stream, verbose)
best_hypervolume_population = pop
# Begin the generational process
for gen in range(1, gens):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, mu, nd='log')
record = stats.compile(pop)
current_hypervolume = hypervolume(pop, ref)
record['hypervolume'] = current_hypervolume
if current_hypervolume > best_hypervolume:
best_hypervolume = current_hypervolume
best_hypervolume_population = pop
logbook.record(gen=gen, evals=len(invalid_ind), **record)
output(logbook.stream, verbose)
return pop, logbook, best_hypervolume_population, best_hypervolume
def _run_geneneration(self,
pop,
crossover_rate,
mutation_rate,
toolbox,
reporting_interval,
allele_dict,
evaluate_population,
keys,
single_obj,
stats_callback,
caching,
**kwargs):
'''
Helper function for runing a single generation.
:param pop:
:param crossover_rate:
:param mutation_rate:
:param toolbox:
:param reporting_interval:
:param allele_dict:
:param evaluate_population:
:param keys:
:param single_obj:
'''
# Variate the population
pop_size = len(pop)
a = pop[0:closest_multiple_of_four(len(pop))]
if single_obj:
offspring = toolbox.select(pop, pop_size, min(pop_size, 10))
else:
offspring = tools.selTournamentDCD(a, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
no_name=False
for child1, child2 in zip(offspring[::2], offspring[1::2]):
# Apply crossover
if random.random() < crossover_rate:
keys = sorted(child1.keys())
try:
keys.pop(keys.index("name"))
except ValueError:
no_name = True
child1_temp = [child1[key] for key in keys]
child2_temp = [child2[key] for key in keys]
toolbox.crossover(child1_temp, child2_temp)
if not no_name:
for child, child_temp in zip((child1, child2),
(child1_temp,child2_temp)):
name = ""
for key, value in zip(keys, child_temp):
child[key] = value
name += " "+str(child[key])
child['name'] = name
else:
for child, child_temp in zip((child1, child2),
(child1_temp,child2_temp)):
for key, value in zip(keys, child_temp):
child[key] = value
#apply mutation
toolbox.mutate(child1, mutation_rate, allele_dict, keys, 0.05)
toolbox.mutate(child2, mutation_rate, allele_dict, keys, 0.05)
for entry in (child1, child2):
del entry.fitness.values
if caching:
for entry in (child1, child2):
try:
ind = stats_callback.tried_solutions[entry]
except KeyError:
del entry.fitness.values
continue
entry.fitness = ind.fitness
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
evaluate_population(invalid_ind, reporting_interval, toolbox, self)
# Select the next generation population
if single_obj:
pop = offspring
else:
pop = toolbox.select(pop + offspring, pop_size)
return pop
def main(archive=[], archivestats={}, **kwargs):
for key, val in kwargs.items():
print(f"{key} : {val}")
seed = kwargs['seed']
random.seed(seed)
np.random.seed(seed)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
NGEN = kwargs['NGEN']
MU = 100 #100
CXPB = 0.9
eta = 20
n_selected = MU if (kwargs['algorithm'] == 'nsga2'
or kwargs['algorithm'] == 'rmota') else int(
(MU * 0.8) / 2)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
nNodes = kwargs['nNodes']
network_creator = topologies.network_topologies[kwargs['network_creator']]
nTasks = kwargs['nTasks']
task_creator = topologies.task_topologies[kwargs['task_creator']]
energy_list = kwargs['energy_list_sim']
networkGraph = network_creator(energy_list=energy_list, **kwargs)
taskGraph = task_creator(networkGraph, **kwargs)
aliveGraph = network_creator(energy_list=energy_list, **kwargs)
#remove_dead_nodes(aliveGraph, energy_list, **kwargs)
try:
pop, toolbox = setup_run(aliveGraph,
taskGraph,
eta,
archive=archive,
archivestats=archivestats,
**kwargs)
except exceptions.NoValidNodeException:
raise exceptions.NetworkDeadException
except exceptions.NetworkDeadException as e:
raise e
except Exception as e:
print(f"Error during ea setup: {e}")
raise e
pop.sort(key=lambda x: x.fitness.values)
#Evaluate the individuals with an invalid fitness
#invalid_ind = [ind for ind in pop if not ind.fitness.valid]
mapped = zip(pop, itertools.repeat(kwargs))
#fitnesses = toolbox.map(toolbox.evaluate, invalid_ind, itertools.repeat([networkGraph,taskGraph, [energy]*25]))
continue_run = checkIfAlive(**kwargs)
if not continue_run:
raise exceptions.NetworkDeadException
try:
fitnesses = toolbox.map(evaluate_wrapper, mapped)
except exceptions.NoValidNodeException:
raise exceptions.NetworkDeadException
except exceptions.NetworkDeadException as e:
raise e
except Exception as e:
print(f"Error during ea gen: {e}")
raise e
for ind, fit in zip(pop, fitnesses):
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'rmota':
ind.fitness.values = fit[:2]
elif kwargs['algorithm'] == 'dtas':
ind.latency = fit[1]
ind.fitness.values = fit[0],
else:
print("unrecognized algorithm")
return -1
ind.received = fit[2]
ind.energy = fit[3]
ind.node_status = fit[4]
# # This is just to assign the crowding distance to the individuals
# # no actual selection is done
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'rmota':
pop = toolbox.select(pop, len(pop))
if kwargs['algorithm'] == 'rmota':
archive, archivestats = toolbox.selArchive(pop, archive, archivestats,
popSize)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'rmota':
offspring = tools.selTournamentDCD(pop, len(pop))
else:
offspring = toolbox.select(pop, n_selected)
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
# if kwargs['verbose']:
# print(f"parents: {ind1} , {ind2}")
# if random.random() <= CXPB:
# toolbox.mate(ind1, ind2)
# if kwargs['verbose']:
# print(f"children: {ind1} , {ind2}")
toolbox.mutate(ind1)
toolbox.mutate(ind2)
# if kwargs['verbose']:
# print(f"mutated: {ind1} , {ind2}")
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
for ind in offspring:
repair_individual(ind, aliveGraph, taskGraph,
kwargs['network_status'])
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
mapped = zip(invalid_ind, itertools.repeat(kwargs))
#fitnesses = toolbox.map(toolbox.evaluate, invalid_ind, itertools.repeat([networkGraph,taskGraph,[energy]*25]))
try:
fitnesses = toolbox.map(evaluate_wrapper, mapped)
except exceptions.NoValidNodeException:
raise exceptions.NetworkDeadException
except exceptions.NetworkDeadException as e:
raise e
except Exception as e:
print(f"error during ea loop: {e}")
raise e
for ind, fit in zip(invalid_ind, fitnesses):
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'rmota':
ind.fitness.values = fit[:2]
elif kwargs['algorithm'] == 'dtas':
ind.latency = fit[1]
ind.fitness.values = fit[0],
else:
print("unrecognized algorithm")
return -1
ind.received = fit[2]
ind.energy = fit[3]
ind.node_status = fit[4]
# Select the next generation population
pop = toolbox.select(pop + offspring, len(pop))
if kwargs['algorithm'] == 'rmota':
archive, archivestats = toolbox.selArchive(pop, archive,
archivestats, popSize)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
#print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'rmota':
pfront = sortEpsilonNondominated(pop, len(pop))[0]
best = tools.selBest(pfront, 1)
else:
best = tools.selBest(pop, 1)
if kwargs['algorithm'] == 'nsga2' or kwargs['algorithm'] == 'dtas':
archive = []
#print("---")
#print(pop)
#print("---")
#print(logbook)
#print("---")
#print(best)
return pop, logbook, best, archive, archivestats
def run_nsga2evo(self, num_run, gen_record, ngen=51,seed=None, population=None, ):
random.seed(seed)
NGEN = ngen
MU = 100
stats = tools.Statistics(lambda ind: ind.fitness.values)
# stats.register("avg", numpy.mean, axis=0)
# stats.register("std", numpy.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
if population == None:
try:
pop = self.toolbox.population_restart()
except FileNotFoundError:
pop = self.toolbox.population(n=MU)
else:
pop = population
# Evaluate the individuals with an invalid fitness
queue = multiprocessing.Queue()
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind, repeat(queue))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = self.toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
# print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
# print('Cloning Population')
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [self.toolbox.clone(ind) for ind in offspring]
# print('Cloning Completed')
# print('Mutating Offspring')
for ind1 in offspring:
self.toolbox.mutate(ind1)
del ind1.fitness.values
# print('Mutation Completed')
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
# print('Evaluating Fitness')
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind, repeat(queue))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# print("Fitness Evaluation Completed")
# Select the next generation population
# print('Selecting Population')
pop = self.toolbox.select(pop + offspring, MU)
# print('Selection Completed')
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
# print(logbook.stream)
# print('Evolution Completed')
filename = str(num_run) + '_' + str(gen_record) + '.csv'
filepath = os.path.join(self.results_folder, filename)
with open(filepath, 'w') as csvfile:
# print('Writing to ' + filename)
writer = csv.writer(csvfile, delimiter=',')
for p in pop:
writer.writerow(p)
# print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
return pop, logbook
def fit(self):
creator.create('FitnessMax', base.Fitness, weights=(1.0, ))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
BOUND_LOW, BOUND_UP = 0.000001, 1.0
NDIM = self._fitness_obj.param_count
toolbox.register('attr_float', self._uniform, BOUND_LOW, BOUND_UP,
NDIM)
toolbox.register('individual', tools.initIterate, creator.Individual,
toolbox.attr_float)
toolbox.register('population', tools.initRepeat, list,
toolbox.individual)
toolbox.register('evaluate', self._fitness_obj.fitness)
toolbox.register('mate',
tools.cxSimulatedBinaryBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0)
toolbox.register('mutate',
tools.mutPolynomialBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0,
indpb=1.0 / NDIM)
toolbox.register("select", tools.selNSGA2)
NGEN = int(self._task_info['NGEN']) + 1
pop_size = int(self._task_info['population'])
CXPB = float(self._task_info['CXPB'])
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register('min', np.min, axis=0)
stats.register('max', np.max, axis=0)
stats.register('mean', np.mean, axis=0)
stats.register('std', np.std, axis=0)
logbook = tools.Logbook()
logbook.header = 'gen', 'evals', 'std', 'min', 'avg', 'max'
pop = toolbox.population(n=pop_size)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop, len(pop))
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, pop_size)
record = stats.compile(pop)
# storage each generation result
self._ga_results_list.append('%s\n' % (record['max'][0]))
logbook.record(gen=gen, evals=len(invalid_ind), **record)
if self._show_opts_history:
print(logbook.stream)
best_ind = tools.selBest(pop, 1)[0]
best_material = self._fitness_obj.recovery_material(best_ind)
return best_material
def train(self):
###################################
# 1. Initialize the population
# toolbox.population은 creator.Individual n개를 담은 list를 반환. (=> population)
###################################
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("[GA] Initialion starts ...")
logging.info("[GA] Initialion starts at " + now_str)
init_start_time = time.time()
GA_history_list = []
start_gen = 1
train_loader, val_loader = get_train_valid_loader_CIFAR10(
self.data_path,
self.args_train.batch_size,
valid_size=0.2,
shuffle=True,
num_workers=self.args_train.workers,
pin_memory=True)
# (train_log 읽어와서 특정 generation부터 이어서 train 하지 않고) 처음부터 train 하는 경우
# population initializatino 부터 GA search 시작
if self.TRAIN_FROM_LOGS == False:
print("Training start!")
logging.info("Training start!")
pop = self.toolbox.population(n=self.pop_size)
###################################
# 2. Evaluate the population
###################################
invalid_ind = [ind for ind in pop]
for idx, ind in enumerate(invalid_ind):
eval_time_for_1_chromo = time.time()
fitness, ind_model = evaluate_one_chromo(
ind,
args_train=self.args_train,
train_loader=train_loader,
val_loader=val_loader,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
eval_time_for_1_chromo = time.time() - eval_time_for_1_chromo
print(
'\t\t [eval_time_for_1_chromo: %.3fs]' %
eval_time_for_1_chromo, idx, 'th chromo is evaluated.')
logging.info(
'\t\t [eval_time_for_1_chromo: %.3fs] %03d th chromo is evaluated.'
% (eval_time_for_1_chromo, idx))
ind.fitness.values = fitness
GA_history_list.append([ind, fitness])
# log 기록 - initialize (= 0th generation)
self.train_log[0] = GA_history_list
self.save_log()
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = self.toolbox.select(pop, len(pop))
# train_log 읽어와서 중간부터 이어서 train 하는 경우
elif self.TRAIN_FROM_LOGS == True:
print("Training start!")
print("Read train_log...")
logging.info("Training start!")
logging.info("Read train_log...")
# train_log 읽어오기
with open(self.REAL_TRAIN_LOG_PATH) as train_log_json_file:
data = json.load(
train_log_json_file) # hp(=hyperparameter), train_log 있음
train_log_past = data['train_log']
niter = len(train_log_past) # 기록 상 총 init 횟수
npop = len(train_log_past['0'])
start_gen = niter # niter = 11 이면, log 상에 0 ~ 10번까지 기록되어있는 것.
# self.train_log 에 읽어온 로그 넣어놓기 (OrderedDict())
for i in range(niter):
self.train_log[str(i)] = train_log_past[str(i)]
self.save_log()
# population 읽어오기
# train_log 에서 last population 읽어오기
last_population = train_log_past[str(int(niter) - 1)]
# last population으로 population 만들기
pop = self.toolbox.population_load(last_population)
# fitness values 도 읽어오기
for i in range(len(last_population)):
pop[i].fitness.values = last_population[i][1]
pop = self.toolbox.select(pop, len(pop))
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("Initialization is finished at", now_str)
logging.info("Initialion is finished at " + now_str)
init_time = time.time() - init_start_time
logging.info("Initialization time = " + str(init_time) + "s")
print()
###################################
# 3. Begin GA
###################################
# Begin the generational process
for gen in range(start_gen, self.ngen + 1): # self.ngen 남은 횟수를 이어서 돌리기
# 3.1. log 기록
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print(str(gen) + "th generation starts at" + now_str)
logging.info(str(gen) + "th generation starts at" + now_str)
start_gen_time = time.time()
# 3.2. Offspring pool 생성 후, crossover(=mate) & mutation
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [self.toolbox.clone(ind) for ind in offspring]
# ::2, 1::2 즉, 짝수번째 크로모좀과 홀수번쨰 크로모좀들 차례로 선택하면서 cx, mut 적용
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
self.toolbox.mate(ind1, ind2, self.cxpb)
self.toolbox.mutate(ind1, mutpb=self.mutpb)
self.toolbox.mutate(ind2, mutpb=self.mutpb)
del ind1.fitness.values, ind2.fitness.values
# 3.3. Evaluation
# Evaluate the individuals with an invalid fitness
print("\t Evaluation...")
start_time = time.time()
# fitness values (= accuracy, flops) 모음
GA_history_list = []
invalid_ind = [ind for ind in offspring]
for idx, ind in enumerate(invalid_ind):
eval_time_for_1_chromo = time.time()
fitness, ind_model = evaluate_one_chromo(
ind,
args_train=self.args_train,
train_loader=train_loader,
val_loader=val_loader,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
# <= evaluate() returns (-prec, flops), NN_model
eval_time_for_1_chromo = time.time() - eval_time_for_1_chromo
print(
'\t\t [eval_time_for_1_chromo: %.3fs]' %
eval_time_for_1_chromo, idx, 'th chromo is evaluated.')
logging.info(
'\t\t [eval_time_for_1_chromo: %.3fs] %03d th chromo is evaluated.'
% (eval_time_for_1_chromo, idx))
ind.fitness.values = fitness
GA_history_list.append([ind, fitness])
# log 기록
self.train_log[gen] = GA_history_list
self.save_log()
eval_time_for_one_generation = time.time() - start_time
print("\t Evaluation ends (Time : %.3f)" %
eval_time_for_one_generation)
# Select the next generation population
pop = self.toolbox.select(pop + offspring, self.pop_size)
gen_time = time.time() - start_gen_time
print('\t [gen_time: %.3fs]' % gen_time, gen,
'th generation is finished.')
logging.info(
'\t Gen [%03d/%03d] -- evals: %03d, evals_time: %.4fs, gen_time: %.4fs'
% (gen, self.ngen, len(invalid_ind),
eval_time_for_one_generation, gen_time))
def nsga2Algorithm(population,
toolbox,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
numOfIndividuals = len(population)
# This is just to assign the crowding distance to the individuals
# no actual selection is done
population = toolbox.select(population, len(population))
record = stats.compile(population) if stats else {}
logbook.record(gen=0, evals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
removed = 0
for gen in range(1, ngen + 1):
# Vary the population
# Dodawanie osobników aż populacja będzie podzielna przez 4
while (len(population) % 4 != 0):
population.append(population[random.randint(
0,
len(population) - 1)])
offspring = tools.selTournamentDCD(population, len(population))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cxpb:
ind1, ind2 = toolbox.mate(ind1, ind2)
if random.random() <= mutpb:
toolbox.mutate(ind1)
if random.random() <= mutpb:
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
removed += halloffame.update(offspring)
# Select the next generation population
population = toolbox.select(population + offspring, numOfIndividuals)
record = stats.compile(population) if stats else {}
logbook.record(gen=gen, evals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
return population, logbook, removed
def test010_(self):
print("**** TEST {} ****".format(whoami()))
NDIM = 30
BOUND_LOW, BOUND_UP = 0.0, 1.0
BOUND_LOW_STR, BOUND_UP_STR = '0.0', '1.0'
RES_STR = '0.01'
NGEN = 250
POPSIZE = 40
MU = 100
CXPB = 0.9
# Create variables
var_names = [str(num) for num in range(NDIM)]
myLogger.setLevel("CRITICAL")
basis_set = [Variable.from_range(name, BOUND_LOW_STR, RES_STR, BOUND_UP_STR) for name in var_names]
myLogger.setLevel("DEBUG")
# Create DSpace
thisDspace = DesignSpace(basis_set)
# Create OSpace
objective_names = ('obj1','obj3')
objective_goals = ('Max', 'Min')
this_obj_space = ObjectiveSpace(objective_names, objective_goals)
mapping = Mapping(thisDspace, this_obj_space)
# Statistics and logging
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
toolbox = base.Toolbox()
#--- Eval
toolbox.register("evaluate", benchmarks.mj_zdt1_decimal)
#--- Operators
toolbox.register("mate", tools.cxSimulatedBinaryBounded,
low=BOUND_LOW, up=BOUND_UP, eta=20.0)
toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP,
eta=20.0, indpb=1.0/NDIM)
toolbox.register("select", tools.selNSGA2)
# Create the population
mapping.assign_individual(Individual2)
mapping.assign_fitness(creator.FitnessMin)
pop = mapping.get_random_population(POPSIZE)
# Evaluate first pop
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
toolbox.map(toolbox.evaluate, invalid_ind)
logging.debug("Evaluated {} individuals".format(len(invalid_ind)))
# Check that they are evaluated
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
assert not invalid_ind
pop = toolbox.select(pop, len(pop))
logging.debug("Crowding distance applied to initial population of {}".format(len(pop)))
myLogger.setLevel("CRITICAL")
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
logging.debug("Selected and cloned {} offspring".format(len(offspring)))
#print([ind.__hash__() for ind in offspring])
#for ind in offspring:
# print()
pairs = zip(offspring[::2], offspring[1::2])
for ind1, ind2 in pairs:
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
logging.debug("Operated over {} pairs".format(len(pairs)))
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
processed_ind = toolbox.map(toolbox.evaluate, invalid_ind)
logging.debug("Evaluated {} individuals".format(len(processed_ind)))
#raise
#for ind, fit in zip(invalid_ind, fitnesses):
# ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
###
with open(r"C:\Users\jon\git\deap1\examples\ga\pareto_front\zdt1_front.json") as optimal_front_data:
optimal_front = json.load(optimal_front_data)
# Use 500 of the 1000 points in the json file
optimal_front = sorted(optimal_front[i] for i in range(0, len(optimal_front), 2))
pop.sort(key=lambda x: x.fitness.values)
print(stats)
print("Convergence: ", convergence(pop, optimal_front))
print("Diversity: ", diversity(pop, optimal_front[0], optimal_front[-1]))
front = np.array([ind.fitness.values for ind in pop])
optimal_front = np.array(optimal_front)
plt.scatter(optimal_front[:,0], optimal_front[:,1], c="r")
print(front)
plt.scatter(front[:,0], front[:,1], c="b")
plt.axis("tight")
#plt.savefig('C:\ExportDir\test1.png')
plt.savefig('C:\\ExportDir\\out.pdf', transparent=True, bbox_inches='tight', pad_inches=0)
#plt.show()
def nsga2vm(seed=None):
random.seed(seed)
# first weight means smaller better, second weight means bigger is better
creator.create("FitnessMin", base.Fitness, weights=(-1.0, 1.0, -1.0))
creator.create("Individual", array, typecode='i', fitness=creator.FitnessMin)
toolbox = base.Toolbox()
# register methods to toolbox
toolbox.register("attr_int", generate_param, INIT_RANDOM)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.attr_int)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", interactive_benchmark, ap_env=ap_env)
toolbox.register("mate", cxSimulatedBinaryBoundedINT, eta=ETA_MATE)
toolbox.register("mutate", mutPolynomialBoundedINT, eta=ETA_MUTATE, indpb=1.0/NDIM)
toolbox.register("select", tools.selNSGA2, nd='log')
# use multithreading
if CPU_THREAD > 1:
pool = multiprocessing.Pool(processes = CPU_THREAD)
toolbox.register("map", pool.map)
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
# display xxxx/4000 and remaining time
progress = ProgressDisplay(NGEN)
# Begin the generational process
for gen in range(NGEN):
if gen == EXIT_INCUBATION_NGEN:
ap_env.is_incubating = False
if gen % 10 == 0:
progress.display_progress(gen)
if WORKLOAD_SIGMA > 0:
ap_env.workload_shuffle_noise(WORKLOAD_SIGMA)
if IDLELOAD_SIGMA > 0:
ap_env.idleload_shuffle_noise(IDLELOAD_SIGMA)
# Vary the population
offspring = tools.selTournamentDCD(pop, N_OFFSPRING)
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
if CPU_THREAD > 1:
pool.close()
pool.join()
pool.terminate()
return pop
def main(cfg):
"""Main workflow of NSGA-II based Scenario analysis."""
random.seed()
pop_size = cfg.nsga2_npop
gen_num = cfg.nsga2_ngens
rule_cfg = cfg.bmps_rule
rule_mth = cfg.rule_method
cx_rate = cfg.nsga2_rcross
mut_perc = cfg.nsga2_pmut
mut_rate = cfg.nsga2_rmut
sel_rate = cfg.nsga2_rsel
ws = cfg.nsga2_dir
worst_econ = cfg.worst_econ
worst_env = cfg.worst_env
# available gene value list
possible_gene_values = list(cfg.bmps_params.keys())
possible_gene_values.append(0)
units_info = cfg.units_infos
slppos_tagnames = cfg.slppos_tagnames
suit_bmps = cfg.slppos_suit_bmps
gene_to_unit = cfg.gene_to_slppos
unit_to_gene = cfg.slppos_to_gene
print_message('Population: %d, Generation: %d' % (pop_size, gen_num))
print_message('BMPs configure method: %s' % ('rule-based' if rule_cfg else 'random-based'))
# create reference point for hypervolume
ref_pt = numpy.array([worst_econ, worst_env]) * multi_weight * -1
stats = tools.Statistics(lambda sind: sind.fitness.values)
stats.register('min', numpy.min, axis=0)
stats.register('max', numpy.max, axis=0)
stats.register('avg', numpy.mean, axis=0)
stats.register('std', numpy.std, axis=0)
logbook = tools.Logbook()
logbook.header = 'gen', 'evals', 'min', 'max', 'avg', 'std'
pop = toolbox.population(cfg, n=pop_size)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
try:
# parallel on multiprocesor or clusters using SCOOP
from scoop import futures
fitnesses = futures.map(toolbox.evaluate, [cfg] * len(invalid_ind), invalid_ind)
# print('parallel-fitnesses: ', fitnesses)
except ImportError or ImportWarning:
# serial
fitnesses = toolbox.map(toolbox.evaluate, [cfg] * len(invalid_ind), invalid_ind)
# print('serial-fitnesses: ', fitnesses)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit[:2]
ind.id = fit[2]
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, pop_size)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print_message(logbook.stream)
# Begin the generational process
output_str = '### Generation number: %d, Population size: %d ###\n' % (gen_num, pop_size)
print_message(output_str)
UtilClass.writelog(cfg.logfile, output_str, mode='replace')
for gen in range(1, gen_num + 1):
output_str = '###### Generation: %d ######\n' % gen
print_message(output_str)
# Vary the population
offspring = tools.selTournamentDCD(pop, int(pop_size * sel_rate))
offspring = [toolbox.clone(ind) for ind in offspring]
# print_message('Offspring size: %d' % len(offspring))
if len(offspring) >= 2: # when offspring size greater than 2, mate can be done
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cx_rate:
if rule_cfg:
toolbox.mate_rule(slppos_tagnames, ind1, ind2)
else:
toolbox.mate_rdn(ind1, ind2)
if rule_cfg:
toolbox.mutate_rule(units_info, gene_to_unit, unit_to_gene, slppos_tagnames,
suit_bmps, ind1,
perc=mut_perc, indpb=mut_rate, method=rule_mth)
toolbox.mutate_rule(units_info, gene_to_unit, unit_to_gene, slppos_tagnames,
suit_bmps, ind2,
perc=mut_perc, indpb=mut_rate, method=rule_mth)
else:
toolbox.mutate_rdm(possible_gene_values, ind1, perc=mut_perc, indpb=mut_rate)
toolbox.mutate_rdm(possible_gene_values, ind2, perc=mut_perc, indpb=mut_rate)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
invalid_ind_size = len(invalid_ind)
# print_message('Evaluate pop size: %d' % invalid_ind_size)
try:
from scoop import futures
fitnesses = futures.map(toolbox.evaluate, [cfg] * invalid_ind_size, invalid_ind)
except ImportError or ImportWarning:
fitnesses = toolbox.map(toolbox.evaluate, [cfg] * invalid_ind_size, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit[:2]
ind.id = fit[2]
# Select the next generation population
pop = toolbox.select(pop + offspring, pop_size)
hyper_str = 'Gen: %d, hypervolume: %f\n' % (gen, hypervolume(pop, ref_pt))
print_message(hyper_str)
UtilClass.writelog(cfg.hypervlog, hyper_str, mode='append')
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print_message(logbook.stream)
# Create plot
front = numpy.array([ind.fitness.values for ind in pop])
plot_pareto_front(front, ['Economic effectiveness', 'Environmental effectiveness'],
ws, gen, 'Pareto frontier of Scenarios Optimization')
# save in file
output_str += 'scenario\teconomy\tenvironment\tgene_values\n'
for indi in pop:
output_str += '%d\t%f\t%f\t%s\n' % (indi.id, indi.fitness.values[0],
indi.fitness.values[1],
str(indi))
UtilClass.writelog(cfg.logfile, output_str, mode='append')
# Delete SEIMS output files, and BMP Scenario database of current generation
delete_model_outputs(cfg.model_dir, cfg.hostname, cfg.port, cfg.bmp_scenario_db)
return pop, logbook
def main(seed=None):
random.seed(seed)
NGEN = 2
MU = 4
CXPB = 0.6
pop = toolbox.population(n=MU)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
stats.register("pop", copy.deepcopy)
history = tools.History()
# Decorate the variation operators
#toolbox.register("variate", variate, mate=toolbox.mate, mutate=toolbox.mutate)
#toolbox.decorate("variate", history.decorator)
toolbox.decorate("mate", history.decorator)
toolbox.decorate("mutate", history.decorator)
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
plt.figure(figsize=(10,4))
plt.subplot(1,2,1)
for ind in pop: plt.plot(ind[0], ind[1], 'k.', ms=3)
plt.xlabel('$x_1$');plt.ylabel('$x_2$');plt.title('Decision space');
plt.subplot(1,2,2)
for ind in pop: plt.plot(ind.fitness.values[0], ind.fitness.values[1], 'k.', ms=3)
plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$');
plt.xlim((0.5,3.6));plt.ylim((0.5,3.6)); plt.title('Objective space');
plt.savefig("objective.png", dpi=200)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "fitness", "size", "pop","ind"
pickle.dump(logbook, open('nsga_ii-results.pickle', 'wb'),
pickle.HIGHEST_PROTOCOL)
hof = tools.ParetoFront()
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
#hof.update(pop)
# This is just to assign the crowding distance to the individualis
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print "Evaluated %i individuals" % len(invalid_ind)
pop = toolbox.select(pop+offspring, len(offspring))
hof.update(pop)
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
plt.close("all")
front = numpy.array([ind.fitness.values for ind in pop])
plt.figure(figsize=(10,10))
#fig,ax = plt.subplots(1,gen)
plt.scatter(front[:,0], front[:,1], c="b")
#locals()["ax"+str(gen)]=plt.scatter(front[:,0], front[:,1], c="b")
#plt.tight_layout()
plt.xlabel("RT(Time)")
plt.ylabel("Memory usage, Mb")
plt.savefig("front_gen"+str(gen)+".png", dpi=200)
print("Pareto individuals are:")
for ind in hof:
print ind, ind.fitness.values
print("XXXXXXXXXX Making plots XXXXXXXXXXXXX")
#fig = plt.figure(figsize=(10,10))
#ax = fig.gca()
#ax.set_xlabel('RT')
#ax.set_ylabel('Memory')
#anim = animation.FuncAnimation(fig, lambda i: animate(i, logbook),
# frames=len(logbook), interval=1,
# blit=True)
#anim.save('nsgaii-geantv.mp4', fps=15, bitrate=-1, dpi=500)
#anim.save('populations.gif', writer='imagemagick')
#print("XXXXXXXXXXXXXXXXXXXXXXX")
print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
print("XXXXXXXXXXX Making more plots XXXXXXXXXXXX")
fronts_s = tools.emo.sortLogNondominated(pop, len(pop))
plot_colors = ('b','r', 'g', 'm', 'y', 'k', 'c')
fig, ax = plt.subplots(1, figsize=(10,10))
for i,inds in enumerate(fronts_s):
par = [toolbox.evaluate(ind) for ind in inds]
df = pd.DataFrame(par)
df.plot(ax=ax, kind='scatter', label='Front ' + str(i+1),
x=df.columns[0], y=df.columns[1],
color=plot_colors[i % len(plot_colors)])
plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$');
plt.savefig("front.png", dpi=200)
def NSGA2(model=errors, NDIM=15, GEN=20, eta=0.5):
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)
toolbox = base.Toolbox()
BOUND_LOW, BOUND_UP = 1.0, 20.0
toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM)
toolbox.register("r_list", tools.initIterate, creator.Individual, toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.r_list)
toolbox.register("evaluate", model)
toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=eta)
toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=eta, indpb=1.0/NDIM)
toolbox.register("select", tools.selNSGA2)
NGEN = GEN
MU = 100
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
min_val=[]
record = stats.compile(pop)
with open('./Pareto_Fronts/NSGA2-'+model.__name__+"-"+str(GEN)+"-"+str(eta)+'.txt', 'w') as f:
f.write("%s %s\n" % (str(record.get('min')[0]), str(record.get('min')[1])))
min_val.append(sum(record.get('min')))
logbook.record(gen=0, evals=len(invalid_ind), **record)
#print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
min_val.append(sum(record.get('min')))
f.write("%s %s\n" % (str(record.get('min')[0]), str(record.get('min')[1])))
logbook.record(gen=gen, evals=len(invalid_ind), **record)
#print(logbook.stream)
return min_val
def train(self):
###################################
# 1. Initialize the population. (toolbox.population은 creator.Individual n개를 담은 list를 반환. (=> population)
###################################
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("[GA] Initialion starts ...")
logging.info("[GA] Initialion starts at " + now_str)
init_start_time = time.time()
# fitness values (= accuracy, flops) 모음
GA_history_list = [] # (choromosome, accuracy, flops) 이렇게 담아놓기
# e.g. [ [[1,3,4], 40, 1000], [[2,6,10], 30%, 2000], ... ]
start_gen = 1
# train_log 읽어와서 중간부터 이어서 train 하지 않고, 처음부터 train 하는 경우
if self.TRAIN_FROM_LOGS == False:
pop = self.toolbox.population(n=self.pop_size)
###################################
# 2. Evaluate the population (with an invalid fitness)
###################################
invalid_ind = [ind for ind in pop]
# pop size = 20 고정
# 크로모좀 4개씩 gpu 하나씩 배정해서 training 하기
for i in [0, 4, 8, 12, 16]:
# i ~ i+1
# i ~ (i+3) 크로모좀을 각각 training 하기 - 먼저 끝났으면 기다리도록.
# 로그도 다르게 떨궈야하네.
eval_time_for_4_chromo = time.time()
# 각각 training
# i ~ i+3
fitness_dict = evaluate_Multiprocess(ind_list=[invalid_ind[i], invalid_ind[i+1], invalid_ind[i+2], invalid_ind[i+3] ],
args_train=self.args_train,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
channels=self.args_train.channels,
log_file_name=self.log_file_name)
# <= evaluate() returns (-prec, flops), NN_model
eval_time_for_4_chromo = time.time() - eval_time_for_4_chromo
print('\t\t [eval_time_for_4_chromo: %.3fs]' % eval_time_for_4_chromo, i, '~', (i+3), 'chromo is evaluated.')
logging.info('\t\t [eval_time_for_4_chromo: %.3fs] %03d ~ %03d th chromo is evaluated.' % (eval_time_for_4_chromo, i, i+3))
for fit_idx, chromo_idx in zip([0, 1, 2, 3], [i, i+1, i+2, i+3]):
fitness_for_idx_chromo = fitness_dict[fit_idx]
invalid_ind[chromo_idx].fitness.values = fitness_for_idx_chromo
GA_history_list.append([invalid_ind[chromo_idx], fitness_for_idx_chromo])
## log 기록 - initialize (= 0th generation)
self.train_log[0] = GA_history_list
self.save_log()
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = self.toolbox.select(pop, len(pop))
# train_log 읽어와서 중간부터 이어서 train 하는 경우
# [Reference] Seeding a population => https://deap.readthedocs.io/en/master/tutorials/basic/part1.html
elif self.TRAIN_FROM_LOGS == True:
print("################# [KYY-check] Read train_log from the middle #################")
logging.info("################# [KYY-check] Read train_log from the middle #################")
# train_log 읽어오기
with open(self.REAL_TRAIN_LOG_PATH) as train_log_json_file:
data = json.load(train_log_json_file) # hp(=hyperparameter), train_log 있음
train_log_past = data['train_log']
niter = len(train_log_past) # 기록 상 총 init 횟수
npop = len(train_log_past['0'])
start_gen = niter # niter = 11 이면, log 상에 0 ~ 10번까지 기록되어있는 것.
# self.train_log 에 읽어온 로그 넣어놓기 (OrderedDict())
for i in range(niter):
self.train_log[str(i)] = train_log_past[str(i)]
self.save_log()
# population 읽어오기
# train_log 에서 last population 읽어오기
last_population = train_log_past[str(int(niter)-1)] # list of [chromosome, [-val_accuracy, flops]]
# last population으로 population 만들기
pop = self.toolbox.population_load(last_population)
# fitness values 도 읽어오기
for i in range(len(last_population)):
pop[i].fitness.values = last_population[i][1] # [-val_accuracy, flops]
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = self.toolbox.select(pop, len(pop))
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("Initialization is finished at", now_str)
logging.info("Initialion is finished at " + now_str)
init_time = time.time() - init_start_time
logging.info("Initialization time = " + str(init_time) + "s")
print()
if self.RANDOM_SEARCH == False:
###################################
# Begin GA
###################################
# Begin the generational process
for gen in range(start_gen, self.ngen+1): # self.ngen 남은 횟수를 이어서 돌리기
##### 3.1. log 기록
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("#####", gen, "th generation starts at", now_str)
logging.info("#####" + str(gen) + "th generation starts at" + now_str)
start_gen_time = time.time()
##### 3.2. Offspring pool 생성 후, crossover(=mate) & mutation
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [self.toolbox.clone(ind) for ind in offspring]
# ::2, 1::2 즉, 짝수번째 크로모좀과 홀수번쨰 크로모좀들 차례로 선택하면서 cx, mut 적용
# e.g. 0번, 1번 ind를 cx, mut // 2번, 3번 ind를 cx, mut // ...
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
# crossover (=mate)
self.toolbox.mate(ind1, ind2, self.cxpb)
# mutation
self.toolbox.mutate(ind1, mutpb=self.mutpb)
self.toolbox.mutate(ind2, mutpb=self.mutpb)
del ind1.fitness.values, ind2.fitness.values
##### 3.3. Evaluation
# Evaluate the individuals with an invalid fitness
print("\t Evaluation...")
start_time = time.time()
# fitness values (= accuracy, flops) 모음
GA_history_list = []
invalid_ind = [ind for ind in offspring]
# pop size = 20 고정
# 크로모좀 4개씩 gpu 하나씩 배정해서 training 하기
for i in [0, 4, 8, 12, 16]:
# i ~ i+1
# i ~ (i+3) 크로모좀을 각각 training 하기 - 먼저 끝났으면 기다리도록.
# 로그도 다르게 떨궈야하네.
eval_time_for_4_chromo = time.time()
# 각각 training
# i ~ i+3
fitness_dict = evaluate_Multiprocess(ind_list=[invalid_ind[i], invalid_ind[i+1], invalid_ind[i+2], invalid_ind[i+3] ],
args_train=self.args_train,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
# <= evaluate() returns (-prec, flops), NN_model
eval_time_for_4_chromo = time.time() - eval_time_for_4_chromo
print('\t\t [eval_time_for_4_chromo: %.3fs]' % eval_time_for_4_chromo, i, '~', (i+3), 'chromo is evaluated.')
logging.info('\t\t [eval_time_for_4_chromo: %.3fs] %03d ~ %03d th chromo is evaluated.' % (eval_time_for_4_chromo, i, i+3))
for fit_idx, chromo_idx in zip([0, 1, 2, 3], [i, i+1, i+2, i+3]):
fitness_for_idx_chromo = fitness_dict[fit_idx]
invalid_ind[chromo_idx].fitness.values = fitness_for_idx_chromo
GA_history_list.append([invalid_ind[chromo_idx], fitness_for_idx_chromo])
# 전체 크로모좀 evaluation 했으니 cuda cache 한번 clear 해주기
torch.cuda.empty_cache()
## log 기록
self.train_log[gen] = GA_history_list
self.save_log()
eval_time_for_one_generation = time.time() - start_time
print("\t Evaluation ends (Time : %.3f)" % eval_time_for_one_generation)
##### Select the next generation population
pop = self.toolbox.select(pop + offspring, self.pop_size)
gen_time = time.time() - start_gen_time
print('\t [gen_time: %.3fs]' % gen_time, gen, 'th generation is finished.')
logging.info('\t Gen [%03d/%03d] -- evals: %03d, evals_time: %.4fs, gen_time: %.4fs' % (
gen, self.ngen, len(invalid_ind), eval_time_for_one_generation, gen_time))
elif self.RANDOM_SEARCH == True:
###################################
# Begin Random Search
###################################
print("###################################")
print("########## Random Search ##########")
print("###################################")
# Begin the generational process
for gen in range(start_gen, self.ngen+1): # self.ngen 남은 횟수를 이어서 돌리기
##### 3.1. log 기록
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("#####", gen, "th generation starts at", now_str)
logging.info("#####" + str(gen) + "th generation starts at" + now_str)
start_gen_time = time.time()
# Random initialization
offspring = self.toolbox.population(n=self.pop_size)
## Evaluation
# Evaluate the individuals with an invalid fitness
print("\t Evaluation...")
start_time = time.time()
# fitness values (= accuracy, flops) 모음
GA_history_list = []
invalid_ind = [ind for ind in offspring]
# pop size = 20 고정
# 크로모좀 4개씩 gpu 하나씩 배정해서 training 하기
for i in [0, 4, 8, 12, 16]:
# i ~ i+1
# i ~ (i+3) 크로모좀을 각각 training 하기 - 먼저 끝났으면 기다리도록.
# 로그도 다르게 떨궈야하네.
eval_time_for_4_chromo = time.time()
# 각각 training
# i ~ i+3
fitness_dict = evaluate_Multiprocess(ind_list=[invalid_ind[i], invalid_ind[i+1], invalid_ind[i+2], invalid_ind[i+3] ],
args_train=self.args_train,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
# <= evaluate() returns (-prec, flops), NN_model
eval_time_for_4_chromo = time.time() - eval_time_for_4_chromo
print('\t\t [eval_time_for_4_chromo: %.3fs]' % eval_time_for_4_chromo, i, '~', (i+3), 'chromo is evaluated.')
logging.info('\t\t [eval_time_for_4_chromo: %.3fs] %03d ~ %03d th chromo is evaluated.' % (eval_time_for_4_chromo, i, i+3))
for fit_idx, chromo_idx in zip([0, 1, 2, 3], [i, i+1, i+2, i+3]):
fitness_for_idx_chromo = fitness_dict[fit_idx]
invalid_ind[chromo_idx].fitness.values = fitness_for_idx_chromo
GA_history_list.append([invalid_ind[chromo_idx], fitness_for_idx_chromo])
# 전체 크로모좀 evaluation 했으니 cuda cache 한번 clear 해주기
torch.cuda.empty_cache()
## log 기록
self.train_log[gen] = GA_history_list
self.save_log()
eval_time_for_one_generation = time.time() - start_time
print("\t Evaluation ends (Time : %.3f)" % eval_time_for_one_generation)
gen_time = time.time() - start_gen_time
print('\t [gen_time: %.3fs]' % gen_time, gen, 'th generation is finished.')
logging.info('\t Gen [%03d/%03d] -- evals: %03d, evals_time: %.4fs, gen_time: %.4fs' % (
gen, self.ngen, len(invalid_ind), eval_time_for_one_generation, gen_time))
def main(problem: Problem = None, seed=None):
config = problem.config
random.seed(seed)
# DEAP framework setup
# We define a bi-objective fitness function.
# 1. Maximize the sparseness minus an amount due to the distance between members
# 2. Minimize the distance to the decision boundary
creator.create("FitnessMulti",
base.Fitness,
weights=config.fitness_weights)
creator.create("Individual",
problem.deap_individual_class(),
fitness=creator.FitnessMulti)
toolbox = base.Toolbox()
problem.toolbox = toolbox
# We need to define the individual, the evaluation function (OOBs), mutation
# toolbox.register("individual", tools.initIterate, creator.Individual)
toolbox.register("individual", problem.deap_generate_individual)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", problem.deap_evaluate_individual)
toolbox.register("mutate", problem.deap_mutate_individual)
toolbox.register("select", tools.selNSGA2)
stats = tools.Statistics(lambda ind: ind.fitness.values)
# stats.register("avg", numpy.mean, axis=0)
# stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "min", "max", "avg", "std"
# Generate initial population.
log.info("### Initializing population....")
pop = toolbox.population(n=config.POPSIZE)
# Evaluate the initial population.
# Note: the fitness functions are all invalid before the first iteration since they have not been evaluated.
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
problem.pre_evaluate_members(invalid_ind)
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
problem.archive.process_population(pop)
# This is just to assign the crowding distance to the individuals (no actual selection is done).
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Initialize the archive.
problem.on_iteration(0, pop, logbook)
# Begin the generational process
for gen in range(1, config.NUM_GENERATIONS):
# invalid_ind = [ind for ind in pop]
# problem.pre_evaluate_members(invalid_ind)
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [ind.clone() for ind in offspring]
problem.reseed(pop, offspring)
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
to_eval = offspring + pop
invalid_ind = [ind for ind in to_eval]
problem.pre_evaluate_members(invalid_ind)
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
problem.archive.process_population(offspring + pop)
# Select the next generation population
pop = toolbox.select(pop + offspring, config.POPSIZE)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
problem.on_iteration(gen, pop, logbook)
return pop, logbook
def train(self):
###################################
# 1. Initialize the population. (toolbox.population은 creator.Individual n개를 담은 list를 반환. (=> population)
###################################
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("[GA] Initialion starts ...")
logging.info("[GA] Initialion starts at " + now_str)
init_start_time = time.time()
pop = self.toolbox.population(n=self.pop_size)
# fitness values (= accuracy, flops) 모음
GA_history_list = [] # (choromosome, accuracy, flops) 이렇게 담아놓기
# e.g. [ [[1,3,4], 40, 1000], [[2,6,10], 30%, 2000], ... ]
###################################
# 2. Evaluate the population (with an invalid fitness)
###################################
invalid_ind = [ind for ind in pop]
for idx, ind in enumerate(invalid_ind):
fitness, ind_model = evaluate_v2(
ind,
args_train=self.args_train,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
ind.fitness.values = fitness
GA_history_list.append([ind, fitness])
## log 기록 - initialize (= 0th generation)
self.train_log[0] = GA_history_list
self.save_log()
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = self.toolbox.select(pop, len(pop))
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("Initialization is finished at", now_str)
logging.info("Initialion is finished at " + now_str)
init_time = time.time() - init_start_time
logging.info("Initialization time = " + str(init_time) + "s")
print()
###################################
# 3. Begin GA
###################################
# Begin the generational process
for gen in range(1, self.ngen):
##### 3.1. log 기록
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d %H:%M:%S')
print("#####", gen, "th generation starts at", now_str)
logging.info("#####" + str(gen) + "th generation starts at" +
now_str)
start_gen = time.time()
##### 3.2. Offspring pool 생성 후, crossover(=mate) & mutation
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [self.toolbox.clone(ind) for ind in offspring]
# ::2, 1::2 즉, 짝수번째 크로모좀과 홀수번쨰 크로모좀들 차례로 선택하면서 cx, mut 적용
# e.g. 0번, 1번 ind를 cx, mut // 2번, 3번 ind를 cx, mut // ...
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= self.cxpb:
self.toolbox.mate(ind1, ind2)
self.toolbox.mutate(ind1, indpb=self.mutpb)
self.toolbox.mutate(ind2, indpb=self.mutpb)
del ind1.fitness.values, ind2.fitness.values
##### 3.3. Evaluation
# Evaluate the individuals with an invalid fitness
print("\t Evaluation...")
start_time = time.time()
# fitness values (= accuracy, flops) 모음
GA_history_list = []
invalid_ind = [ind for ind in offspring]
for idx, ind in enumerate(invalid_ind):
fitness, ind_model = evaluate_v2(
ind,
args_train=self.args_train,
stage_pool_path_list=self.stage_pool_path_list,
data_path=self.data_path,
log_file_name=self.log_file_name)
# <= evaluate() returns (-prec, flops), NN_model
ind.fitness.values = fitness
GA_history_list.append([ind, fitness])
## log 기록
self.train_log[gen] = GA_history_list
self.save_log()
eval_time_for_one_generation = time.time() - start_time
print("\t Evaluation ends (Time : %.3f)" %
eval_time_for_one_generation)
##### Select the next generation population
pop = self.toolbox.select(pop + offspring, self.pop_size)
gen_time = time.time() - start_gen
print('\t [gen_time: %.3fs]' % gen_time, gen,
'th generation is finished.')
logging.info(
'\t Gen [%03d/%03d] -- evals: %03d, evals_time: %.4fs, gen_time: %.4fs'
% (gen, self.ngen, len(invalid_ind),
eval_time_for_one_generation, gen_time))
def main(rand_seed=None):
random.seed(rand_seed)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "min", "max", "avg", "std"
# Generate initial population.
print("### Initializing population ....")
population = toolbox.population(n=POPSIZE)
# Evaluate the individuals with an invalid fitness.
# Note: the fitnesses are all invalid before the first iteration since they have not been evaluated
invalid_ind = [ind for ind in population]
# fitnesses = toolbox.map(toolbox.evaluate, invalid_ind, itertools.repeat(population))
# Note: the sparseness is calculated wrt the archive. It can be calculated wrt population+archive
# Therefore, we pass to the evaluation method the current archive.
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind,
itertools.repeat(archive.get_archive()))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update archive with the individuals on the decision boundary.
for ind in population:
if ind.misclass < 0:
archive.update_archive(ind)
print("### Number of Individuals generated in the initial population: " +
str(Individual.COUNT))
# This is just to assign the crowding distance to the individuals (no actual selection is done).
population = toolbox.select(population, len(population))
record = stats.compile(population)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population.
offspring = tools.selTournamentDCD(population, len(population))
offspring = [toolbox.clone(ind) for ind in offspring]
# Reseeding
if len(archive.get_archive()) > 0:
seed_range = random.randrange(1, RESEEDUPPERBOUND)
candidate_seeds = archive.archived_seeds
for i in range(seed_range):
population[len(population) - i -
1] = reseed_individual(candidate_seeds)
# Mutation.
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals
# NOTE: all individuals in both population and offspring are evaluated to assign crowding distance.
invalid_ind = [ind for ind in population + offspring]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind,
itertools.repeat(archive.get_archive()))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
for ind in population + offspring:
if ind.fitness.values[1] < 0:
archive.update_archive(ind)
# Select the next generation population
population = toolbox.select(population + offspring, POPSIZE)
if gen % 300 == 0:
archive.create_report(x_test, Individual.SEEDS, gen)
# Update the statistics with the new population
record = stats.compile(population)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
print(logbook.stream)
return population
def main(seed=None, play=0, NGEN=40, MU=4 * 10):
# random.seed(seed)
# MU has to be a multiple of 4. period.
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values[1])
# stats.register("avg", numpy.mean, axis=0)
# stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
toolbox.register("evaluate", minimize_src)
time1 = time.time()
pop_src = toolbox.population(n=MU)
time2 = time.time()
print("After population initialisation", time2 - time1)
print(type(pop_src))
# print("population initialized")
# network_obj = Neterr(indim, outdim, n_hidden, np.random)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop_src if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
time3 = time.time()
print("After feedforward", time3 - time2)
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop_src = toolbox.select(pop_src, len(pop_src))
# print( "first population selected, still outside main loop")
# print(pop)
record = stats.compile(pop_src)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
maxi = 0
stri = ''
flag = 0
# Begin the generational process
# print(pop.__dir__())
time4 = time.time()
for gen in range(1, NGEN):
# Vary the population
if gen == 1:
time6 = time.time()
if gen == NGEN - 1:
time7 = time.time()
print()
print("here in gen no.", gen)
offspring = tools.selTournamentDCD(pop_src, len(pop_src))
offspring = [toolbox.clone(ind) for ind in offspring]
if play:
if play == 1:
pgen = NGEN * 0.1
elif play == 2:
pgen = NGEN * 0.9
if gen == int(pgen):
print("gen:", gen, "doing clustering")
to_bp_lis = cluster.give_cluster_head(offspring,
int(MU * bp_rate))
assert (to_bp_lis[0] in offspring)
print("doing bp")
[
item.modify_thru_backprop(indim,
outdim,
network_obj_src.rest_setx,
network_obj_src.rest_sety,
epochs=10,
learning_rate=0.1,
n_par=10) for item in to_bp_lis
]
# Evaluate the individuals with an invalid fitness
invalid_ind = [
ind for ind in offspring if not ind.fitness.valid
]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if gen == 1:
time8 = time.time()
if gen == NGEN - 1:
time9 = time.time()
dum_ctr = 0
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
flag = 0
if random.random() <= CXPB:
ind1, ind2 = toolbox.mate(ind1, ind2, gen)
ind1 = creator.Individual(indim, outdim, ind1)
ind2 = creator.Individual(indim, outdim, ind2)
flag = 1
maxi = max(maxi, ind1.node_ctr, ind2.node_ctr)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
offspring[dum_ctr] = ind1
offspring[dum_ctr + 1] = ind2
del offspring[dum_ctr].fitness.values, offspring[dum_ctr +
1].fitness.values
dum_ctr += 2
if gen == 1:
print("1st gen after newpool", time.time() - time8)
if gen == NGEN - 1:
print("last gen after newpool", time.time() - time9)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop_src = toolbox.select(pop_src + offspring, MU)
record = stats.compile(pop_src)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
anost = logbook.stream
liso = [item.rstrip() for item in anost.split("\t")]
mse = float(liso[3])
print(anost)
stri += anost + '\n'
print("generation done")
# file_ob.write(str(logbook.stream))
# print(len(pop))
# file_ob.close()
time5 = time.time()
print("Overall time", time5 - time4)
# print(stri)
print(
' ------------------------------------src done------------------------------------------- '
)
fronts = tools.sortNondominated(pop_src, len(pop_src))
pareto_front = fronts[0]
print(pareto_front)
st = '\n\n'
pareto_log_fileo = open(
"./log_folder/log_pareto_just_src_nll_mse_misc_com" + str(NGEN) +
".txt", "a")
for i in range(len(pareto_front)):
print(pareto_front[i].fitness.values)
st += str(pareto_front[i].fitness.values)
pareto_log_fileo.write(st + '\n')
pareto_log_fileo.close()
return pop_src, logbook
def multirun(datasetName):
# init_population,init_ari,datamat,datalabels = ini_Cluster(kNumber=6) #多种聚类算法产生初始种群
# datamat,datalabels = loadDataset("../dataset/glass.data")
path = '../dataset/'+datasetName
datamat,datalabels = loadDataset(path)
print 'data ready'
# datalabels_to_float = list(map(lambda x: float(x), datalabels))
sampledData, remainedData, sampledIndex, remainedIndex= data_sample(datamat,1,10)
print 'sampledData ready'
#
pop_kmeans = rsnn(sampledData, remainedData, sampledIndex, remainedIndex,'kmeans')
print 'kmeans end'
max_nmi1 = -inf
for ind1 in pop_kmeans:
nmi1 = normalized_mutual_info_score(datalabels, ind1)
if nmi1 > max_nmi1:
max_nmi1 = nmi1
print '初始kmeans最大nmi为%s'%max_nmi1
pop_ward = rsnn(sampledData, remainedData, sampledIndex, remainedIndex,'ward')
print 'ward end'
max_nmi2 = -inf
for ind2 in pop_ward:
nmi2 = normalized_mutual_info_score(datalabels, ind2)
if nmi2 > max_nmi2:
max_nmi2 = nmi2
print '初始ward最大nmi为%s'%max_nmi2
pop_complete = rsnn(sampledData, remainedData, sampledIndex, remainedIndex,'complete')
print 'complete end'
max_nmi3 = -inf
for ind3 in pop_complete:
nmi3 = normalized_mutual_info_score(datalabels, ind3)
if nmi3 > max_nmi3:
max_nmi3 = nmi3
print '初始complete最大nmi为%s'%max_nmi3
pop_average = rsnn(sampledData, remainedData, sampledIndex, remainedIndex,'average')
print 'average end'
max_nmi4 = -inf
for ind4 in pop_average:
nmi4 = normalized_mutual_info_score(datalabels, ind4)
if nmi4 > max_nmi4:
max_nmi4 = nmi4
print '初始average最大nmi为%s'%max_nmi4
pop = []
pop.extend(pop_kmeans)
pop.extend(pop_ward)
pop.extend(pop_complete)
pop.extend(pop_average)
init_population = []
for indiv1 in pop:
ind1 = creator.Individual(indiv1)
init_population.append(ind1)
filter_pop = filter(lambda x:len(x)>0,init_population) ##去除初始化聚类失败的结果
population = filter_pop #population是总的种群，后续的交叉算法的结果也要添加进来
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, tile(datamat,(len(invalid_ind),1,1)),tile(population,(len(invalid_ind),1,1)),invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
population = toolbox.select(population, len(population))
popeliteLen = len(population)
for i in range(generation):
print '第%s代'%i
popElite = toolbox.select(population, popeliteLen) #top half from population
# Vary the population
parentSpring = tools.selTournamentDCD(population, len(population))
parentSpring = [toolbox.clone(ind) for ind in parentSpring]
newoffspring = []
# applying crossover
subpopArr=[]
for subtimes in range(5): #这里循环10次,是因为subpop写死成４次，乘起来又产生４０个个体，用于产生dsce运算产生４０个结果，视作交叉
subpopOneArr = getSubPop(parentSpring)
subpopArr.extend(subpopOneArr)
for subpop in subpopArr:
transMatrix, popClusterArr_3, popClusterArr_2, clusterNumArr = transformation(datamat, subpop)
similiarMatrix, unionClusterArr_2 = measureSimilarity(transMatrix, popClusterArr_3, popClusterArr_2,
clusterNumArr, datamat, a1=0.8)
dictCownP = assign(similiarMatrix, 0.7)
resultList = resultTransform(dictCownP, datamat)
ind_ensemble = creator.Individual(resultList)
newoffspring.append(ind_ensemble)
# evaluating fitness of individuals with invalid fitnesses
invalid_ind = [ind for ind in newoffspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, tile(datamat,(len(invalid_ind),1,1)),tile(newoffspring,(len(invalid_ind),1,1)),invalid_ind)#这里只用了未经处理的数据,没有用到真实类别
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Chossing a population for the next generation
population = toolbox.select(popElite + newoffspring, popeliteLen)
result1 = toolbox.nondominated(population,len(population))
ari_arr = []
max_ari = -inf
for ind in result1[0]:
ari = adjusted_rand_score(datalabels, ind)
ari_arr.append(ari)
if ari > max_ari:
max_ari = ari
nmi_arr = []
max_nmi = -inf
print 'nmi值'
for ind in result1[0]:
nmi = normalized_mutual_info_score(datalabels, ind)
nmi_arr.append(nmi)
if nmi > max_nmi:
max_nmi = nmi
print '最大nmi值为:%s' % max_nmi
return max_nmi,max_ari
def main_spea2(algorithm="SPEA2", seed=None, NGEN=100, MU=100):
random.seed(seed)
#NGEN # Generation
#MU # Population Size
CXPB = 0.9
NDIM = 30
BOUND_LOW = (B_RP[0], S_RP[0], B_IP[0], S_IP[0], Tb[0], TbB[0], Ts[0],
TsB[0], bCinc[0], bBinc[0], sCdec[0], sBdec[0], Kb[0], Ks[0],
Offset[0])
BOUND_UP = (B_RP[1], S_RP[1], B_IP[1], S_IP[1], Tb[1], TbB[1], Ts[1],
TsB[1], bCinc[1], bBinc[1], sCdec[1], sBdec[1], Kb[1], Ks[1],
Offset[1])
toolbox.register("mate",
tools.cxSimulatedBinaryBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=0.0)
toolbox.register("mutate",
tools.mutPolynomialBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=0.0,
indpb=1.0 / NDIM)
toolbox.register("select", tools.selSPEA2)
toolbox.register("evaluate", evaluateInd)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
#algorithm = "NSGA2"
print "Algorithm = ", algorithm
print "Generation = ", NGEN
print "Population Size = ", MU
referencePoint = (2.0, 0, 0)
hv = InnerHyperVolume(referencePoint)
#front = [(1.0, 0.5128205128205128, 195.0),(1.0, 2.7732997481108312, 397.0),(1.0, 0.7787356321839081, 348.0),(1.0, 2.7732997481108312, 397.0),(1.0, 0.5339233038348082, 339.0),(1.0, 0.5128205128205128, 195.0),(1.0, 0.5128205128205128, 195.0),(1.0, 0.5128205128205128, 195.0)]
global identifier
statfile_purchase = genFileName(algorithm, "purchase", NGEN, MU,
identifier) # Stat Generation
insert(statfile_purchase,
algorithm + "_gen" + str(NGEN) + "_pop" + str(MU))
statfile_utility = genFileName(algorithm, "utility", NGEN, MU,
identifier) # Stat Generation
insert(statfile_utility, algorithm + "_gen" + str(NGEN) + "_pop" + str(MU))
statfile_time = genFileName(algorithm, "time", NGEN, MU,
identifier) # Stat Generation
insert(statfile_time, algorithm + "_gen" + str(NGEN) + "_pop" + str(MU))
"""
print ("Initial Population")
for p in pop:
print p
"""
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
oldpop = pop
oldrecord = record
#map(lambda x:insert(statfile_purchase,str(x.fitness.values[0])),oldpop)
#map(lambda x:insert(statfile_utility,str(x.fitness.values[1])),oldpop)
#map(lambda x:insert(statfile_time,str(x.fitness.values[2])),oldpop)
#Only final generation required
#volume = hv.compute([x.fitness.values for x in oldpop if x.fitness.values[0]==1.0])
#print volume
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
if stop_early(oldpop, pop, gen, oldrecord, record):
print("Stopping generation early, no purchases")
break
map(lambda x: insert(statfile_purchase, str(x.fitness.values[0])), pop)
insertnl(statfile_purchase)
map(lambda x: insert(statfile_utility, str(x.fitness.values[1])), pop)
insertnl(statfile_utility)
map(lambda x: insert(statfile_time, str(x.fitness.values[2])), pop)
insertnl(statfile_time)
r = redis.StrictRedis(host='152.46.19.201', port=6379, db=0)
r.flushall() # Remove all keys once done
return pop, logbook
def mainNSGA(seed=None):
with open("evaluated_inds.log", "w") as myfile:
myfile.write("===Evaluated genomes===:\n")
with open("runtime_pareto.log", "w") as myfile:
myfile.write("===runtime_pareto===:\n")
with open("runtime_time.log", "w") as myfile:
myfile.write("===runtime_time===:\n")
ga_data = {}
toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate",
tools.mutUniformInt,
low=knobs_low,
up=knobs_up,
indpb=0.05)
toolbox.register("select", tools.selNSGA2)
random.seed(seed)
#MU = 40
#CXPB = 0.8
#MUTPB = 0.8
#NGEN = 30
MU = 80
CXPB = 0.8
MUTPB = 0.8
NGEN = 30
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
# Begin the generational process
for gen in range(1, NGEN):
start = time.time()
# Vary the population
print(" ======Beginning %i th generation======: " % gen)
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
del ind1.fitness.values, ind2.fitness.values
if random.random() <= MUTPB:
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
fitness_list = []
fronts_lists = tools.sortNondominated(pop,
len(pop),
first_front_only=True)[0]
fronts = []
for i in range(len(fronts_lists)):
if fronts_lists[i] not in fronts:
fronts.append(fronts_lists[i])
fitness_list.append(fronts_lists[i].fitness.values)
print " Pareto front is:"
ga_data[gen] = {"fitness": fitness_list, "front": fronts}
with open("runtime_pareto.log", "a") as myfile:
myfile.write(str(ga_data[gen]) + "\n")
end = time.time()
with open("runtime_time.log", "a") as myfile:
myfile.write(str(end - start) + "\n")
pprint.pprint(fitness_list)
pprint.pprint(fronts)
print(" Evaluated %i individuals\n" % len(invalid_ind))
jsonConfigFile = "./ga_data.json"
with open(jsonConfigFile, "w") as jFile:
json.dump(ga_data, jFile, indent=4, separators=(',', ': '))
#with open(jsonConfigFile) as jFile:
# ga_data = json.load(jFile)
# pprint.pprint(ga_data)
print("-- End of (successful) evolution --")
return pop
def NSGA(funcs_l, weights, var, sigma, MU=24, NGEN=20, wide_search=1.5):
IND_SIZE = len(var)
creator.create("MaFitness", base.Fitness, weights=weights)
creator.create("Individual", list, fitness=creator.MaFitness)
toolbox = base.Toolbox()
eval_funcs = lambda x: tuple([f(x) for f in funcs_l])
toolbox.register("evaluate", eval_funcs)
S.model = var
c = S.extract_genotype()
init_func = lambda c, sigma, size: np.random.normal(c, sigma, size)
bound_max = list(wide_search * c)
bound_min = list(-wide_search * c)
toolbox.register("attr_float", init_func, c, sigma, len(var))
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
paretofront = tools.ParetoFront()
#toolbox.register("mutate", tools.mutGaussian, mu=c, sigma=sigma, indpb=1.0 / IND_SIZE)
toolbox.register("mutate",
tools.mutPolynomialBounded,
low=bound_min,
up=bound_max,
eta=20.0,
indpb=1.0 / IND_SIZE)
toolbox.register("mate",
tools.cxSimulatedBinaryBounded,
low=bound_min,
up=bound_min,
eta=20.0)
toolbox.register("select", tools.selNSGA2)
CXPB = 0.6
turing_spot = tools.Statistics(lambda ind: ind.fitness.values[0])
rectanglitude = tools.Statistics(lambda ind: ind.fitness.values[1])
mstats = tools.MultiStatistics(Rectanglitude=rectanglitude,
Turing_Spot=turing_spot)
mstats.register("avg", np.mean, axis=0)
mstats.register("max", np.max, axis=0)
mstats.register("min", np.min, axis=0)
logbook = tools.Logbook()
pop = toolbox.population(n=MU)
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop = toolbox.select(pop, len(pop))
record = mstats.compile(pop)
#print("Record =" ,record)
logbook.record(**record)
print(logbook.stream)
# Begin the generational process
records = {}
for gen in range(1, NGEN):
# Vary the population
if (gen % 5 == 0):
S.Swarm.controller.withVisiblite(True)
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = mstats.compile(pop)
logbook.record(g=gen, **record)
records[gen] = record
print(logbook.stream)
return pop, paretofront, records
def NSGA2(model):
print "Model: ", model.__name__
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin)
toolbox = base.Toolbox()
model = model()
BOUND_LOW, BOUND_UP = model.domMin, model.domMax
# Functions zdt4 has bounds x1 = [0, 1], xn = [-5, 5], with n = 2, ..., 10
# BOUND_LOW, BOUND_UP = [0.0] + [-5.0]*9, [1.0] + [5.0]*9
# Functions zdt1, zdt2, zdt3 have 30 dimensions, zdt4 and zdt6 have 10
NDIM = model.decs
def uniform(low, up, size=None):
try:
return [random.uniform(a, b) for a, b in zip(low, up)]
except TypeError:
return [random.uniform(a, b) for a, b in zip([low] * size, [up] * size)]
toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM)
toolbox.register("r_list", tools.initIterate, creator.Individual, toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.r_list)
toolbox.register("evaluate", model.getObjs)
toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0)
toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0, indpb=1.0 / NDIM)
toolbox.register("select", tools.selNSGA2)
min_val = []
# random.seed(seed)
NGEN = 25
MU = 100
CXPB = 0.9
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
pop = toolbox.population(n=MU)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
min_val.append(sum(record.get('min')))
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, NGEN):
# Vary the population
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
min_val.append(sum(record.get('min')))
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
# print("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
best = pop[0]
for i in pop:
if sum(model.getObjs(i)) < sum(model.getObjs(best)):
best = i
print "Best solution = "
for i in best:
print i
return min_val
def main():
dolphin_dir = find_dolphin_dir()
pad_path = dolphin_dir + '/Pipes/p3'
mw_path = dolphin_dir + '/MemoryWatcher/MemoryWatcher'
if dolphin_dir is None:
print('Could not find dolphin config dir.')
return
state = p3.state.State()
sm = p3.state_manager.StateManager(state)
write_locations(dolphin_dir, sm.locations())
fox = p3.fox.Fox()
# Weights: (Minimize damage on self, Maximize damage dealt)
creator.create("FitnessOptima", base.Fitness, weights=(-1.0, 1.0))
creator.create("Individual", list, fitness=creator.FitnessOptima)
toolbox = base.Toolbox()
BOUND_LOW, BOUND_UP = -1.0, 1.0
NDIM = 30
def uniform(low, up, size=None):
try:
return [random.uniform(a, b) for a, b in zip(low, up)]
except TypeError:
return [
random.uniform(a, b) for a, b in zip([low] * size, [up] * size)
]
toolbox.register("attr_real", random.uniform, -1, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_real, c.nnet['n_weights'])
toolbox.register("population",
tools.initRepeat,
list,
toolbox.individual,
n=c.game['n_agents'])
toolbox.register("evaluate", evalANN)
toolbox.register("mate",
tools.cxSimulatedBinaryBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0)
toolbox.register("mutate",
tools.mutPolynomialBounded,
low=BOUND_LOW,
up=BOUND_UP,
eta=20.0,
indpb=1.0 / NDIM)
toolbox.register("select", tools.selNSGA2)
CXPB, MU, NGEN = 0.9, 12, 2000
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean, axis=0)
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
# pop = toolbox.population()
# to load in trained weights:
with open("population.txt", "r") as infile:
pop = json.load(infile)
# Add agents to the population
for ind in pop:
ann = nnet(c.nnet['n_inputs'], c.nnet['n_h_neurons'],
c.nnet['n_outputs'], ind)
fox.add_agent(ann)
print('Start dolphin now. Press ^C to stop p3.')
with p3.pad.Pad(pad_path) as pad, p3.memory_watcher.MemoryWatcher(
mw_path) as mw:
fox.agent = 11
run(fox, state, sm, mw, pad, pop,
toolbox) # run agent once to get past "ready, GO!" lag
fox.agent = 0
run(fox, state, sm, mw, pad, pop, toolbox)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = toolbox.evaluate(fox.agents)
for ind, fit in zip(invalid_ind, fitnesses[0]):
ind.fitness.values = tuple(fit)
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
try:
# Begin the generational process
while fox.generation < NGEN:
fox.generation += 1
fox.reset()
# Selection
# offspring = toolbox.select(pop, len(pop))
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
# Mating
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(child1, child2)
# Mutation
toolbox.mutate(child1)
toolbox.mutate(child2)
del child1.fitness.values, child2.fitness.values
# Add offspring to NNs
for ind in offspring:
ann = nnet(c.nnet['n_inputs'], c.nnet['n_h_neurons'],
c.nnet['n_outputs'], ind)
fox.add_agent(ann)
# Run game with offspring
with p3.pad.Pad(pad_path) as pad, p3.memory_watcher.MemoryWatcher(
mw_path) as mw:
run(fox, state, sm, mw, pad, offspring, toolbox)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.evaluate(fox.agents)
for ind, fit in zip(invalid_ind, fitnesses[0]):
ind.fitness.values = tuple(fit)
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
# Collect stats
record = stats.compile(pop)
logbook.record(gen=fox.generation,
evals=len(invalid_ind),
**record)
print(logbook.stream)
if (fox.generation % 1 == 0):
statistics(pop, logbook, fox.generation)
print("Training complete")
except KeyboardInterrupt:
print('Stopped')
def main(num_Gens, size_Pops, cx, seed=None):
# toolbox.register("attr_float", iniPops)
# toolbox.register("individual", tools.initIterate, creator.Individuals, toolbox.attr_float)
# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# toolbox.register("evaluate", calBenefitandCost)
# toolbox.register("mate", tools.cxOnePoint)
# toolbox.register("mutate", mutModel, indpb=MutateRate)
# toolbox.register("select", tools.selNSGA2)
random.seed(seed)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
# stats.register("avg", numpy.mean, axis=0)
# stats.register("std", numpy.std, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "min", "max"
pop = toolbox.population(n=size_Pops)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
try:
# parallel on multiprocesor or clusters using SCOOP
from scoop import futures
fitnesses = futures.map(toolbox.evaluate, invalid_ind)
# print "parallel-fitnesses: ",fitnesses
except ImportError or ImportWarning:
# serial
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
# print "serial-fitnesses: ",fitnesses
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# This is just to assign the crowding distance to the individuals
# no actual selection is done
pop = toolbox.select(pop, size_Pops)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
# Begin the generational process
for gen in range(1, num_Gens):
printInfo("###### Iteration: %d ######" % gen)
# Vary the population
offspring = tools.selTournamentDCD(pop, int(size_Pops * SelectRate))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= cx:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
try:
# parallel on multiprocesor or clusters using SCOOP
from scoop import futures
fitnesses = futures.map(toolbox.evaluate, invalid_ind)
except ImportError or ImportWarning:
# serial
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
# invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
# fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Select the next generation population
pop = toolbox.select(pop + offspring, size_Pops)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
# print "\nlogbook.stream: ", logbook.stream
if gen % 1 == 0:
# Create plot
createPlot(pop, model_Workdir, num_Gens, size_Pops, gen)
# save in file
outputStr = "### Generation number: %d, Population size: %d ###" % (num_Gens, size_Pops) + LF
outputStr += "### Generation_%d ###" % gen + LF
outputStr += "cost\tbenefit\tscenario" + LF
for indi in pop:
outputStr += str(indi.fitness.values[0]) + "\t" + str(indi.fitness.values[1]) + "\t" \
+ str(indi) + LF
outfilename = model_Workdir + os.sep + "NSGAII_OUTPUT" + os.sep + "Gen_" \
+ str(GenerationsNum) + "_Pop_" + str(PopulationSize) + os.sep + "Gen_" \
+ str(GenerationsNum) + "_Pop_" + str(PopulationSize) + "_resultLog.txt"
WriteLog(outfilename, outputStr, MODE='append')
printInfo("Final population hypervolume is %f" % hypervolume(pop, [11.0, 11.0]))
return pop, logbook