def evalDeme(deme):
deme[:] = [toolbox.clone(ind) for ind in toolbox.select(deme, len(deme))]
# algorithms_helper.varLambda(toolbox, deme, LAMBDA, 0.5, 0.3)
algorithms.varOr(deme, toolbox, LAMBDA, 0.5, 0.3)
for ind in deme:
ind.fitness.values = toolbox.evaluate(ind)
return deme
def run_evolution(population, ngen, file=None):
cxpb, mutpb = 0.0, 1
if not file is None:
file.write("\nGeneration 0\n")
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
if not file is None:
file.write(str(sortmap(ind)) + ", fitness: " + "{:,}".format(fit[0]) + "\n")
file.flush()
population = toolbox.select(population, 1)
for i in range(ngen):
file.write("\nGeneration " + str(i+1) + "\n")
offspring = algorithms.varOr(population, toolbox, 4, cxpb, mutpb)
fitnesses = toolbox.map(toolbox.evaluate, offspring)
for ind, fit in zip(offspring, fitnesses):
ind.fitness.values = fit
if not file is None:
file.write(str(sortmap(ind)) + ", fitness: " + "{:,}".format(fit[0]) + "\n")
file.flush()
np = toolbox.select(offspring, 1)
if np[0].fitness.values[0] <= population[0].fitness.values[0]:
population = np
return population
def run_ga(self, initial_pop=False, plot=False):
if initial_pop != False:
population = initial_pop
else:
population = self.toolbox.population(n=self.initial_pop_size)
fitnesses = list(map(self.toolbox.evaluate, population))
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
self.all_pops.append(population)
for n in tqdm(range(self.generations)):
self.all_pops.append(population)
offspring = algorithms.varOr(population,
self.toolbox,
cxpb=self.cxpb,
mutpb=self.mutpb,
lambda_=self.num_children)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
new_population = population + offspring
population = self.toolbox.select(new_population,
k=self.num_to_select)
if plot == True:
self.model.plot_substrate(self.metrics.substrate)
self.model.plot_substrate(self.metrics.product)
plt.show()
def eaMuPlusLambda1Gen(population, toolbox, mu, lambda_, cxpb, mutpb, gen):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:param gen: The generation number.
:returns: The offspring and population after 1 generation.
The algorithm takes in a population and evolves it in place using the
:meth:`varOr` method. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution (if
any). The logbook will contain the generation number, the number of
evaluations for each generation and the statistics if a
:class:`~deap.tools.Statistics` if any. The *cxpb* and *mutpb* arguments
are passed to the :func:`varAnd` function. The pseudocode goes as follow
::
evaluate(population)
for g in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(population + offspring, mu)
First, the individuals having an invalid fitness are evaluated. Second,
the evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by the :func:`varOr` function. The
offspring are then evaluated and the next generation population is
selected from both the offspring **and** the population. Finally, when
*ngen* generations are done, the algorithm returns a tuple with the final
population and a :class:`~deap.tools.Logbook` of the evolution.
.. note::
Care must be taken when the lambda:mu ratio is 1 to 1 as a non-stochastic
selection will result in no selection at all as
the operator selects *lambda* individuals from a pool of *mu*.
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
# 1 iteration of eaMuPlusLambda (deap.__version__ == '1.0')
if gen != 0:
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
else:
# Generation 0
offspring = population
return offspring, population
def main_parallel():
Component.resetPfKeeping()
Component.resetCostKeeping()
manager = Manager()
Component.pfkeeping = manager.dict(Component.pfkeeping)
Component.costkeeping = manager.dict(Component.costkeeping)
pool = Pool(processes=3)
toolbox.register("map", pool.map)
print "MULTIOBJECTIVE OPTIMIZATION: parallel version"
start_delta_time = time.time()
# optimization
random.seed(64)
npop = 100
ngen = 50
stats = tools.Statistics(key=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", "avg", "std", "min", "max"
pop = toolbox.population(n=npop)
fits = toolbox.map(toolbox.evaluate, pop)
for fit,ind in zip(fits, pop):
ind.fitness.values = fit
nevals = npop
allpop = []
for gen in range(ngen):
allpop = allpop+pop
record = stats.compile(pop)
logbook.record(gen=gen, evals=nevals, **record)
print(logbook.stream)
offspring = algorithms.varOr(pop, toolbox, lambda_=npop, cxpb=0.5, mutpb=0.1)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
nevals = len(invalid_ind)
fits = toolbox.map(toolbox.evaluate, invalid_ind)
for fit,ind in zip(fits, invalid_ind):
ind.fitness.values = fit
pop = toolbox.select(offspring+pop, k=npop)
front = toolbox.sort(allpop, k=int(ngen*npop), first_front_only=True)
pool.close()
pool.join()
delta_time = time.time() - start_delta_time
print 'DONE: {} s'.format(str(datetime.timedelta(seconds=delta_time)))
return allpop, logbook, front
def eaMuPlusLambdaEarlyStop(
population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
"""
See the documentation regarding :class:`~deap.algorithms.eaMuPlusLambdaEarlyStop`.
This strategy uses the `earlystop' callable in the toolbox to determine if
the evolution must stop before the last generation is reached.
"""
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
gen = 0
winner = toolbox.select(population, 1)[0]
earlystop = toolbox.earlystop()
while gen < ngen and not earlystop(winner):
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
winner = toolbox.select(population, 1)[0]
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
gen += 1
return population, logbook
def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, halloffame=None):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda\_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:returns: Yields the population and the logbook
First, the individuals having an invalid fitness are evaluated. Then, the
evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by a crossover, a mutation or a
reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 -
(cxpb + mutpb). The offspring are then evaluated and the next generation
population is selected from both the offspring **and** the population.
Briefly, the operators are applied as following ::
evaluate(population)
for i in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(population + offspring, mu)
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
# 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
yield population, invalid_ind
# Begin the generational process
while True:
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
yield population, invalid_ind
def launch_es(mu=100, lambda_=200, cxpb=0.6, mutpb=0.3, ngen=1000, display=False, verbose=False):
# Initialisation
random.seed()
population = toolbox.population(n=mu)
populations.append(population)
halloffame = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
# Evaluate the entire population
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Boucle de l'algorithme évolutionniste
for gen in range(1, ngen + 1):
### A completer pour implementer un ES en affichant regulièrement les resultats a l'aide de la fonction plot_results fournie ###
### Vous pourrez tester plusieurs des algorithmes implémentés dans DEAP pour générer une population d'"enfants"
### à partir de la population courante et pour sélectionner les géniteurs de la prochaine génération
offspring = algorithms.varOr(population, toolbox, lambda_=100, cxpb=0.5,mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring + population, k=100)
populations.append(population)
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(population)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
return population, logbook, halloffame,populations
def main(dictionary, g1, g2, g3):
global features
preprocess_data(dictionary, g1, g2, g3)
data = pd.read_csv('student-mat-pre.csv', delimiter = ';')
cols = list(data.columns)
cols.remove('G1')
cols.remove('G2')
cols.remove('G3')
for col in cols:
data[col] = (data[col] - data[col].mean()) / data[col].std(ddof=0)
data.to_csv('temp')
features = []
with open('temp') as f:
for index, line in enumerate(f):
params = line.strip().split(',')
if index != 0:
for i in range(len(params)):
params[i] = float(params[i])
features.append(params[1:-3])
features = features[1:len(features)]
tree_c = DecisionTreeClassifier(criterion='entropy')
gnb_c = GaussianNB()
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('bit', random.random)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.bit, n=30)
toolbox.register('population',tools.initRepeat, list, toolbox.individual, n=200)
toolbox.register('evaluate', fitness_value)
toolbox.register('mate', tools.cxUniform, indpb=0.1)
toolbox.register('mutate', tools.mutFlipBit, indpb=0.05)
toolbox.register('select', tools.selNSGA2)
population = toolbox.population()
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = (fit,)
for gen in range(100):
offspring = algorithms.varOr(population, toolbox, lambda_ = 10, cxpb=0.5, mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = (fit,)
population = toolbox.select(offspring+population, k=20)
individual = tools.selBest(population, k=1)
def eaMuPlusLambda(population,
toolbox,
mu,
lambda_,
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in tqdm(range(1, ngen + 1)):
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
return population, logbook
def eaMuCommaLambda1Gen(population, toolbox, mu, lambda_, cxpb, mutpb, gen):
"""This is the :math:`(\mu~,~\lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:param gen: The generation number.
:returns: The offspring and population after 1 generation.
First, the individuals having an invalid fitness are evaluated. Then, the
evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by a crossover, a mutation or a
reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 -
(cxpb + mutpb). The offspring are then evaluated and the next generation
population is selected **only** from the offspring. Briefly, the operators
are applied as following ::
evaluate(population)
for i in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(offspring, mu)
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
# 1 iteration of eaMuCommaLambda (deap.__version__ == '1.0')
assert lambda_ >= mu, ('lambda ({}) must be greater or equal to mu '
'({}).'.format(lambda_, mu))
if gen != 0:
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
else:
# Generation 0
offspring = population
return offspring, population
def eaMuPlusLambda_redefined(population, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN):
for i in range(NGEN):
# ----determine the offspring and evaluate the fitness,w
offspring = algorithms.varOr(population, toolbox, LAMBDA, CXPB, MUTPB)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# ---- evaluate fitness values for the population
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
population = toolbox.select(offspring + population, MU)
return population
def debug_eval(self):
toolbox.register("evaluate", evalr2, self.y, self.basetable)
toolbox.register("mate", tools.cxOnePoint) #Uniform, indpb=0.5)
toolbox.register("mutate", mutRan, indpb=self.mut)
toolbox.register("select", tools.selBest)
population=toolbox.population()
fits=toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits,population):
ind.fitness.values=fit
offspring=algorithms.varOr(population, toolbox, lambda_=100, cxpb=.5, mutpb=.05)
#print "offspring",offspring
#fits=toolbox.map(toolbox.evaluate, offspring)
print offspring
for ind in offspring:
ind.fitness.values=toolbox.evaluate(ind)
print ind
print ind.fitness.values
def eaMuPlusLambda(toolbox, ngen, verbose=__debug__,
include_parents_in_next_generation=True):
population = toolbox.population
halloffame = toolbox.hof
gen = -1
current_seed = -1
for gen in range(toolbox.initial_generation, ngen):
record_individuals(toolbox, population)
extra = []
if halloffame.items:
extra = list(map(toolbox.clone, random.sample(population, toolbox.conf.extra_from_hof)))
offspring = varOr(population + extra, toolbox, toolbox.conf.lambda_, 1 - toolbox.conf.mutpb, toolbox.conf.mutpb)
for ind in offspring:
# just a little extra for when we later want to analyze the hof manually
ind.generation = gen
if include_parents_in_next_generation:
for ind in population:
del ind.fitness.values
candidates = population + offspring
else:
candidates = offspring
nevals, total_steps, current_seed = evaluate_candidates(candidates, toolbox)
if halloffame is not None:
halloffame.update(offspring)
record = toolbox.stats.compile(candidates) if toolbox.stats is not None else {}
population[:] = toolbox.select(candidates, toolbox.conf.mu)
toolbox.logbook.record(gen=gen, nevals=nevals, steps=total_steps, **record)
if verbose:
print(toolbox.logbook.stream)
if toolbox.checkpoint:
toolbox.checkpoint(data=dict(generation=gen, halloffame=halloffame, population=population,
logbook=toolbox.logbook, last_seed=current_seed, strategy=None,
recorded_individuals=toolbox.recorded_individuals))
toolbox.final_checkpoint_data = dict(generation=gen, halloffame=halloffame, population=population,
logbook=toolbox.logbook, last_seed=current_seed, strategy=None,
recorded_individuals=toolbox.recorded_individuals)
return toolbox.logbook
def eaMuPlusLambda_redefined(population, toolbox, MU, LAMBDA, CXPB, MUTPB,
NGEN):
for i in range(NGEN):
# ----determine the offspring and evaluate the fitness,w
offspring = algorithms.varOr(population, toolbox, LAMBDA, CXPB, MUTPB)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# ---- evaluate fitness values for the population
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
population = toolbox.select(offspring + population, MU)
return population
def debug_eval(self):
toolbox.register("evaluate", evalr2, self.y, self.basetable)
toolbox.register("mate", tools.cxOnePoint) # Uniform, indpb=0.5)
toolbox.register("mutate", mutRan, indpb=self.mut)
toolbox.register("select", tools.selBest)
population = toolbox.population()
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = fit
offspring = algorithms.varOr(population,
toolbox,
lambda_=100,
cxpb=0.5,
mutpb=0.05)
for ind in offspring:
ind.fitness.values = toolbox.evaluate(ind)
print(ind)
print(ind.fitness.values)
def run_evolution_strategy(X, train_y_bin, Xt, test_y_bin, train_min,
train_max, cxpb, mutpb, start_population_size,
size_of_offspring, number_of_epochs):
creator.create("FitnessMin", base.Fitness, weights=(-1.0, ))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register("individual_guess", initIndividual, creator.Individual)
toolbox.register("population_guess", initPopulation, list,
toolbox.individual_guess)
toolbox.register("mate", tools.cxSimulatedBinary, eta=1)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.decorate("mate", checkBounds(train_min, train_max))
toolbox.decorate("mutate", checkBounds(train_min, train_max))
population = toolbox.population_guess(train_min, train_max,
start_population_size)
hof = tools.HallOfFame(1)
avg_error_on_population = []
hof_errors = []
test_errors = []
hofs = []
for i in range(number_of_epochs):
indyvidual_errors = []
offspring = algorithms.varOr(population, toolbox, size_of_offspring,
cxpb, mutpb)
for indyvidual in offspring:
indyvidual_errors.append(
update_loss_of_indyvidual(indyvidual, X, train_y_bin,
train_min, train_max, offspring, hof,
True))
population[:] = tools.selBest(offspring, start_population_size)
avg_error_on_population.append(np.mean(indyvidual_errors))
hof_rmse = update_loss_of_indyvidual(hof[0], X, train_y_bin, train_min,
train_max, offspring, hof, False)
hof_errors.append(hof_rmse)
test_error = update_loss_of_indyvidual(hof[0], Xt, test_y_bin,
train_min, train_max, offspring,
hof, False)
test_errors.append(test_error)
hofs.append(hof[0])
print("Epoch : {} avg RMSE for population : {} hof : {}".format(
i + 1, avg_error_on_population[-1], hof[0]))
return hofs, hof_errors, test_errors
def evolvepara(self):
#import multiprocessing
#pool = multiprocessing.Pool()
#toolbox.register("map", pool.map)
toolbox.register("genind", self.mkeind,self.indsize)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.genind)
toolbox.register("population",tools.initRepeat, list, toolbox.individual, n=self.popsize)
toolbox.register("evaluate", self.evalr2)
toolbox.register("mate", tools.cxOnePoint) #Uniform, indpb=0.5)
toolbox.register("mutate", self.mutaRan)#, indpb=self.mut)
toolbox.register("select", tools.selBest)
population=toolbox.population()
#print population
fits=toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits,population):
ind.fitness.values=fit
#print fit, ind
#print fit
#offspring=algorithms.varOr(population, toolbox, lambda_=100, cxpb=.5, mutpb=.05)
#print toolbox.map(toolbox.evaluate, offspring)
avgfitnesses=[]
for gen in range(self.ngen):
offspring=algorithms.varOr(population, toolbox, lambda_=self.popsize, cxpb=self.cx, mutpb=self.mut)
#print "offspring",offspring
#fits=toolbox.map(toolbox.evaluate, offspring)
for ind in offspring:
ind.fitness.values=toolbox.evaluate(ind)
#for fit, ind in zip(fits,population):
# ind.fitness.values=fit
population=toolbox.select([k for k,v in itert.groupby(sorted(offspring+population))], k=100)
popfits = toolbox.map(toolbox.evaluate, population)
avgfitnesses.append(np.mean(popfits))
#plot(len(avgfitnesses), list(avgfitnesses))
# print avgfitnesses
print toolbox.map(toolbox.evaluate, population)
return population
def evolverf(self,evalfunc="q2loo"):
#toolbox.register("map", pool.map)
toolbox.register("genind", self.mkeindrf,self.indsize)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.genind)
toolbox.register("population",tools.initRepeat, list, toolbox.individual, n=self.popsize)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.genind)
toolbox.register("population",tools.initRepeat, list, toolbox.individual, n=self.popsize)
if evalfunc=="q2loo":
toolbox.register("evaluate", self.evalq2loo)
elif evalfunc=="r2":
toolbox.register("evaluate", self.evalr2)
elif evalfunc=="r2adj":
toolbox.register("evaluate", self.evalr2adj)
else:
raise ValueError("not a valid evaluation function specified; use evalr2adj, evalr2, or q2loo")
toolbox.register("mate", tools.cxOnePoint) #Uniform, indpb=0.5)
toolbox.register("mutate", self.mutaRan)#, indpb=self.mut)
toolbox.register("select", tools.selBest)
population=toolbox.population()
#print population
fits=toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits,population):
ind.fitness.values=fit
avgfitnesses=[]
for gen in range(self.ngen):
offspring=algorithms.varOr(population, toolbox, lambda_=self.popsize, cxpb=self.cx, mutpb=self.mut)
for ind in offspring:
ind.fitness.values=toolbox.evaluate(ind)
population=toolbox.select([k for k,v in itert.groupby(sorted(offspring+population))], k=100)
popfits = toolbox.map(toolbox.evaluate, population)
avgfitnesses.append(np.mean(popfits))
return population
def sample_from_pop(population, toolbox, lambda_, cxpb, mutpb):
"""Generate a set of individuals from a population.
Generate a set of individuals from a population. Parameters:
:param population: the population to start from
:param toolbox: the DEAP framework toolbox that contains the variation operators and the evaluation function
:param lambda_: number of individuals to generate
:param cxpb: cross-over probability (set to 0 to test only mutation)
:param mutbp: mutation probability
WARNING: if cxpb>0, the population size needs to be >2 (it thus won't work to sample individuals from a single individual)
"""
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
#for of in offspring:
# print("Offspring: "+str(of))
# Evaluate the individuals with an invalid fitness
fitnesses = toolbox.map(toolbox.evaluate, offspring)
for ind, fit in zip(offspring, fitnesses):
ind.fitness.values = fit[0]
ind.bd = fit[1]
ind.evolvability_samples=None # SD: required, otherwise, the memory usage explodes... I do not understand why yet.
return offspring
def eaMuPlusLambdaTol(population,
toolbox,
mu,
lambda_,
ngen,
cxpb,
mutpb,
tol,
stats=None,
halloffame=None,
verbose=__debug__):
global cxpb_orig, mutpb_orig
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda\_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:param ngen: The number of generation.
:param stats: A :class:`~deap.tools.Statistics` object that is updated
inplace, optional.
:param halloffame: A :class:`~deap.tools.HallOfFame` object that will
contain the best individuals, optional.
:param verbose: Whether or not to log the statistics.
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution.
The algorithm takes in a population and evolves it in place using the
:func:`varOr` function. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
logbook will contain the generation number, the number of evalutions for
each generation and the statistics if a :class:`~deap.tools.Statistics` is
given as argument. The *cxpb* and *mutpb* arguments are passed to the
:func:`varOr` function. The pseudocode goes as follow ::
evaluate(population)
for g in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(population + offspring, mu)
First, the individuals having an invalid fitness are evaluated. Second,
the evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by the :func:`varOr` function. The
offspring are then evaluated and the next generation population is
selected from both the offspring **and** the population. Finally, when
*ngen* generations are done, the algorithm returns a tuple with the final
population and a :class:`~deap.tools.Logbook` of the evolution.
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
min_fit = np.array(logbook.chapters["fitness"].select("min"))
# Begin the generational process
flag_change = False
flag_limit = False
gen = 1
while gen < ngen + 1 and not (min_fit[-1] <= tol).all():
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
min_fit = np.array(logbook.chapters["fitness"].select("min"))
min_actual = np.array(
logbook.chapters["fitness"].select("min"))[-1]
min_old = np.array(logbook.chapters["fitness"].select("min"))[-2]
if verbose:
print(logbook.stream)
if (abs(min_actual - min_old) <
0.01).all() and flag_limit is False:
cxpb = cxpb - 0.01
mutpb = mutpb + 0.01
print("change")
flag_change = True
if cxpb < 0.4:
cxpb = 0.4
mutpb = 0.5
print("limits")
flag_limit = True
else:
cxpb = cxpb_orig
mutpb = mutpb_orig
print("back to orig")
if flag_change is True:
cxpb = cxpb_orig + 0.1
mutpb = mutpb_orig - 0.15
flag_change = False
print("orig after change")
flag_limit = False
gen += 1
return population, logbook
def main():
# reset bookkeeping
Component.resetCostKeeping()
Component.resetPfKeeping()
Component.resetRiskKeeping()
## use existing pf data
#pfkeeping = np.load('pfkeeping.npz')
#Component.pfkeeping['flexure'] = pfkeeping['flexure']
#Component.pfkeeping['shear'] = pfkeeping['shear']
#Component.pfkeeping['deck'] = pfkeeping['deck']
## use existing cost data
#costkeeping = np.load('costkeeping.npz')
#Component.costkeeping['flexure'] = costkeeping['flexure']
#Component.costkeeping['shear'] = costkeeping['shear']
#Component.costkeeping['deck'] = costkeeping['deck']
manager = Manager()
Component.pfkeeping = manager.dict(Component.pfkeeping)
Component.costkeeping = manager.dict(Component.costkeeping)
Component.riskkeeping = manager.dict(Component.riskkeeping)
pool = Pool(processes=num_processes)
toolbox.register("map", pool.map)
print "MULTIOBJECTIVE OPTIMIZATION: parallel version"
start_delta_time = time.time()
# optimization
random.seed(64)
logbook = tools.Logbook()
logbook.header = ["gen", "evals", "nfront", "mean", "tol"]
pop = toolbox.population(n=NPOP)
fits = toolbox.map(toolbox.evaluate, pop)
for fit,ind in zip(fits, pop):
ind.fitness.values = fit
nevals = NPOP
g = 1
distances = []
frontfitlast = np.zeros((1,2))
nevalsum = 0
evolStop = False
halloffame = tools.ParetoFront()
while not evolStop:
offspring = algorithms.varOr(pop, toolbox, lambda_=NPOP, cxpb=0.9, mutpb=0.1)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
nevals = len(invalid_ind)
nevalsum += nevals
fits = toolbox.map(toolbox.evaluate, invalid_ind)
for fit,ind in zip(fits, invalid_ind):
ind.fitness.values = fit
pop = toolbox.select(offspring+pop, k=NPOP)
front = toolbox.sort(offspring+pop, k=NPOP, first_front_only=True)[0]
halloffame.update(front)
# check if stop evolution
distance=[]
frontfit = [ind.fitness.values for ind in halloffame]
for obj in frontfit:
vector = np.array(frontfitlast)-np.array(obj)
distance.append(min(np.linalg.norm(vector, axis=1)))
distances.append(np.mean(distance))
longest = 0.
for point1 in frontfit:
for point2 in frontfit:
dist = np.linalg.norm(np.array(point1)-np.array(point2))
if dist > longest:
longest = dist
tol = longest/NPOP
tol = np.maximum(tol, TOL)
evolStop = (len(distances)>NGEN and all(np.array(distances[-NGEN:])<tol)) or g>NMAX
frontfitlast = frontfit
# Gather all the fitnesses in one list and print the stats
record = stats.compile(pop)
logbook.record(gen=g, evals=nevals,nfront=len(halloffame),
mean=distances[-1], tol=tol, **stats.compile(pop))
print(logbook.stream)
g+=1
pool.close()
pool.join()
delta_time = time.time() - start_delta_time
print 'DONE: {} s'.format(str(datetime.timedelta(seconds=delta_time)))
return pop, logbook, halloffame, nevalsum
def gaMuPlusLambda(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
logbook = algorithms.tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
# Evaluate the fitness before starting
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
print("_________________GA GEN: " + str(gen) +
"______________________")
# Invalidate fitness of whole pop to train and evaluate
for ind in population:
ind.GAN.gen_no = gen
ind.GAN.offspring = 0
del ind.fitness.values
# Train and Evaluate the individuals
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
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = []
for ind in offspring:
if not ind.fitness.valid:
ind.GAN.offspring = 1
invalid_ind.append(ind)
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
return population, logbook
def myEAMuCommaLambda(population, startgen, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, logbook=None, verbose=False, id=None):
assert lambda_ >= mu, "lambda must be greater or equal to mu."
# 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)
if logbook is None:
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=startgen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
total_time = datetime.timedelta(seconds=0)
for gen in range(startgen+1, ngen):
start_time = datetime.datetime.now()
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
if gen % 1 == 0:
# Fill the dictionary using the dict(key=value[, ...]) constructor
cp = dict(population=population, generation=gen, halloffame=halloffame,
logbook=logbook, rndstate=random.getstate())
if id is None:
cp_name = "checkpoint_es.pkl"
else:
cp_name = "checkpoint_es_{}.pkl".format(id)
pickle.dump(cp, open(cp_name, "wb"))
gen_time = datetime.datetime.now() - start_time
total_time = total_time + gen_time
if total_time > datetime.timedelta(hours=4*24):
print("Time limit exceeded.")
break
return population, logbook
def OnEvolve(self,
cxpb=0.6,
mutpb=0.2,
lambda_=n_pop * 2,
verbose=__debug__):
if self.gen == 0:
start_time = time.time()
invalid_ind = [
ind for ind in self.population 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
if self.halloffame is not None:
self.halloffame.update(self.population)
record = self.stats.compile(self.population) if self.stats else {}
self.logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
elapsed_time = time.time() - start_time
#print self.logbook.stream + "\t\t%0.2f sec"%(elapsed_time)
#self.Log('\n'+self.logbook.stream)
self.context.evo_time = elapsed_time
self.gen += 1
self.selected_individuals = self.halloffame[:1]
# save to file
#self.Checkpoint()
else:
start_time = time.time()
offspring = algorithms.varOr(self.population, self.toolbox,
lambda_, cxpb, mutpb)
invalid_ind = [
ind for ind in offspring
] # if not ind.fitness.valid] # force eval of every indv, as history is a moving widnow to eval on
fitnesses = self.toolbox.map(self.toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if self.halloffame is not None:
self.halloffame.clear(
) # force eval of every indv, as history is a moving widnow to eval on
self.halloffame.update(offspring)
self.population[:] = self.toolbox.select(
self.population + offspring, n_pop)
# Append the current generation statistics to the logbook
record = self.stats.compile(self.population) if self.stats else {}
self.logbook.record(gen=self.gen,
nevals=len(invalid_ind),
**record)
if verbose:
elapsed_time = time.time() - start_time
#print self.logbook.stream + "\t\t%0.2f sec"%(elapsed_time)
#self.Log('\n'+self.logbook.stream)
self.context.evo_time = elapsed_time
self.gen += 1
self.selected_individuals = self.halloffame[:1]
# save to file
#self.Checkpoint()
# using the selected best item
#signal = self.evalLive(self.halloffame[0])
# but with pareto front we have ANY number of non dominated individuals each gen, just use them all as an ensemble model
signal = stats.mode([self.evalLive(indv)
for indv in self.halloffame]).mode[0]
self.context.Log(str(self.gen) + ' : ' + str(self.halloffame[0]))
return signal
def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
div=[] #for measuring diversity
resetPeaks()
logbook = tools.Logbook()
logbook2 = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
logbook2.header = ['gen', 'nevals'] + (stats.fields if stats else [])
fitnesses = []
hofPop=toolbox.map(toolbox.evaluateHof,population) #change depending on eval func
for ind, fit in zip(population, hofPop):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats is not None else {}
div.append(momOfInertia(population))
logbook2.record(gen=0, nevals=len(population), **record)
#Evaluate the individuals with an invalid fitness
#fitnesses = []
#for i in xrange(len(population)):
# fitnesses.append(toolbox.evaluate((population)[i],population))
#for ind, fit in zip(population, fitnesses):
# ind.fitness.values = fit
population=toolbox.evaluate(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(population), **record)
if verbose:
print logbook.stream
bog=[]
offline=[]
offlinegen=[]
accuracy=[]
leaps=0
#stability=[0]
#needed for accuracy
maxt=100 #highest peak
mint=0
# Begin the generational process
for gen in range(1, ngen+1):
#move the peaks
if gen == 150 or gen ==300:
print("Best individual is: %s\nwith fitness: %s" % (halloffame[0], halloffame[0].fitness))
#print population
halloffame.clear()
#bog=[]
global peaks
peaks[0]=bitFlip(peaks[0],0.1)
peaks[2]=bitFlip(peaks[2],0.1)
#if gen==150:
# peaks[1]=80
# peaks[3]=100
#if gen==300:
# peaks[1]=100
# peaks[3]=80
for x in xrange(len(population)):
del population[x].fitness.values
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluateHof, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the fitness of all individuals for clearing
clearingFit=toolbox.map(toolbox.evaluateHof,population + offspring)
for ind, fit in zip(population+offspring, clearingFit):
ind.fitness.values = fit
#all individuals given fitness for hof this is then used to clear
#fitnesses = []
#for i in xrange(len(population+offspring)):
# fitnesses.append(toolbox.evaluate((population+offspring)[i],population+offspring))
#for ind, fit in zip(population+offspring, fitnesses):
# ind.fitness.values = fit
tempPop=population+offspring
tempPop = toolbox.evaluate(tempPop)
# Select the next generation population
population[:] = toolbox.select(tempPop, mu)#change temppop here if it doesnt work
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population + offspring), **record)
if verbose:
print logbook.stream
# Update the hall of fame with the generated individuals fitness not dependant on sharing
hofPop = deepcopy(population)
fitnesses=toolbox.map(toolbox.evaluateHof,hofPop) #change depending on eval func
for ind, fit in zip(hofPop, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
try:
a=halloffame[0].fitness.values
except IndexError:
a=0
halloffame.update(hofPop)
b=halloffame[0].fitness.values
if b>a:
leaps+=1
#record stats and offline and diversity and accuracy
record = stats.compile(hofPop) if stats is not None else {}
div.append(momOfInertia(hofPop))
logbook2.record(gen=gen, nevals=len(hofPop), **record)
offline.append(halloffame[0].fitness.values[0])
offlinegen.append(gen)
accuracy.append((record["max"]-mint)/(maxt-mint))
#show graphs at final generation
if gen==ngen:
#open files for data
off=open("offline.txt","a")
bog=open("bog.txt","a")
mini=open("min.txt","a")
aver=open("aver.txt","a")
maxi=open("max.txt","a")
diver=open("div.txt","a")
print leaps
print "offline performance: ",sum(offline)/len(offline)
print "average bog: ", sum(logbook.select("max"))/len(logbook.select("max"))
#print offline
off.write(str(sum(offline)/len(offline)))
off.write(",")
bog.write(str(sum(logbook.select("max"))/len(logbook.select("max"))))
bog.write(",")
diver.write(str(div))
diver.write(",")
mini.write(str(logbook2.select("min")))
mini.write(",")
aver.write(str(logbook2.select("avg")))
aver.write(",")
maxi.write(str(logbook2.select("max")))
maxi.write(",")
#accuracy
plt.figure(2)
plt.title("accuracy")
plt.plot(offlinegen,accuracy)
plt.axis([0,ngen,0,1])
plt.show()
#diversity
plt.figure(3)
plt.title("Diversity")
plt.plot(xrange(len(div)),div)
plt.axis([0,len(div),0,1250])
plt.show()
#min av max
plt.figure(4)
plt.title("min, avg, max")
plt.plot(xrange(ngen+1),logbook2.select("min"),label="min")
plt.plot(xrange(ngen+1),logbook2.select("avg"),label="avg")
plt.plot(xrange(ngen+1),logbook2.select("max"),label="max")
plt.legend(loc=8)
plt.axis([0,ngen,0,maxt])
plt.show()
#stability
plt.figure(5)
plt.title("Stability")
stability=[]
for i in xrange(len(accuracy)):
stability.append(max(0,accuracy[i]-accuracy[i-1]))
plt.plot(offlinegen,stability)
plt.axis([0,ngen,0,1])
plt.show()
off.close()
bog.close()
mini.close()
aver.close()
maxi.close()
diver.close()
return population, logbook
def evolve(self, evalfunc="q2loo"):
"""
1st element returned is the original population,
2nd is is the evaluation of the firness function on the originalpopualtion,
3rd is the final population,
4th is the evalution of the fitness function on the final population.
"""
toolbox.register("genind", self.mkeindseed, self.indsize)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.genind)
toolbox.register("population",
tools.initRepeat,
list,
toolbox.individual,
n=self.popsize)
if evalfunc == "q2loo":
toolbox.register("evaluate", self.evalq2loo)
elif evalfunc == "q2lmo":
toolbox.register("evaluate", self.evalq2lmo)
elif evalfunc == "r2":
toolbox.register("evaluate", self.evalr2)
elif evalfunc == "r2adj":
toolbox.register("evaluate", self.evalr2adj)
else:
raise ValueError(
"not a valid evaluation function specified; use evalr2adj, evalr2, or q2loo"
)
toolbox.register("mate", tools.cxOnePoint) #Uniform, indpb=0.5)
toolbox.register("mutate", self.mutaRan) #, indpb=self.mut)
toolbox.register("select", tools.selBest)
#progress bar start!
#print 'Starting... # GEN FINISHED:',
origpop = toolbox.population()
#self.mkeindseed.count=0
population = cp.deepcopy(origpop)
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = fit
avgfitnesses = []
popfits = 0
#prb=ProgressBar(self.ngen)
for gen in range(self.ngen):
try:
offspring = algorithms.varOr(population,
toolbox,
lambda_=self.popsize,
cxpb=self.cx,
mutpb=self.mut)
for ind in offspring:
ind.fitness.values = toolbox.evaluate(ind)
population = toolbox.select([
k for k, v in itert.groupby(sorted(offspring + population))
],
k=100)
popfits = toolbox.map(toolbox.evaluate, population)
#prb.animate(gen)
#prb.score=np.mean(popfits)
#ProgressBar.score=property(lambda self: self.score+np.mean(popfits))
#prb.update_time(1, prb.score)
except (KeyboardInterrupt, SystemExit):
result = [
origpop,
toolbox.map(toolbox.evaluate, origpop), population,
toolbox.map(toolbox.evaluate, population)
]
return result #self.pretty_print(returnobj)
result = [
origpop,
toolbox.map(toolbox.evaluate, origpop), population,
toolbox.map(toolbox.evaluate, population)
]
return self.pretty_print(result)
def novelty_ea(evaluate, params, pool=None):
"""Novelty Search algorithm
Novelty Search algorithm. Parameters:
:param evaluate: the evaluation function
:param params: the dict of run parameters
* params["pop_size"]: the number of parent individuals to keep from one generation to another
* params["lambda"]: the number of offspring to generate, given as a multiplying coefficent on params["pop_size"] (the number of generated individuals is: params["lambda"]*params["pop_size"]
* params["nb_gen"]: the number of generations to compute
* params["stats"]: the statistics to use (from DEAP framework))
* params["stats_offspring"]: the statistics to use (from DEAP framework))
* params["variant"]: the different supported variants ("NS", "Fit", "NS+Fit", "NS+BDDistP", "NS+Fit+BDDistP"), add "," at the end of the variant to select in offspring only (as the ES "," variant). By default, selects within the parents and offspring. "NS" uses the novelty criterion, "Fit" the fitness criterion and "BDDistP' the distance to the parent in the behavior space. If a single criterion is used, an elitist selection scheme is used. If more than one criterion is used, NSGA-II is used (see build_toolbox_ns function)
* params["cxpb"]: the crossover rate
* params["mutpb"]: the mutation rate
:param dump_period_bd: the period for dumping behavior descriptors
:param dump_period_pop: the period for dumping the current population
:param evolvability_period: period of the evolvability computation
:param evolvability_nb_samples: the number of samples to generate from each individual in the population to estimate their evolvability (WARNING: it will significantly slow down a run and it is used only for statistical reasons
"""
print("Novelty search algorithm")
alphas = params[
"alphas"] # parameter to compute the alpha shape, to estimate the distance to explored area
variant = params["variant"]
if ("+" in variant):
emo = True
else:
emo = False
nb_eval = 0
lambda_ = int(params["lambda"] * params["pop_size"])
toolbox = build_toolbox_ns(evaluate, params, pool)
population = toolbox.population(n=params["pop_size"])
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals']
if (params["stats"] is not None):
logbook.header += params["stats"].fields
if (params["stats_offspring"] is not None):
logbook.header += params["stats_offspring"].fields
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
nb_eval += len(invalid_ind)
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
# fit is a list of fitness (that is also a list) and behavior descriptor
for ind, fit in zip(invalid_ind, fitnesses):
ind.fit = fit[
0] # fit is an attribute just used to store the fitness value
ind.parent_bd = None
ind.bd = listify(fit[1])
ind.id = generate_uuid()
ind.parent_id = None
for ind in population:
ind.am_parent = 0
archive = updateNovelty(population, population, None, params)
alpha_shape = alphashape.alphashape(archive.all_bd, alphas)
isortednov = sorted(range(len(population)),
key=lambda k: population[k].novelty,
reverse=True)
varian = params["variant"].replace(",", "")
for i, ind in enumerate(population):
ind.dist_to_explored_area = dist_to_shapes(ind.bd, alpha_shape)
ind.rank_novelty = isortednov.index(i)
ind.dist_to_parent = 0
if (emo):
if (varian == "NS+Fit"):
ind.fitness.values = (ind.novelty, ind.fit[0])
elif (varian == "NS+BDDistP"):
ind.fitness.values = (ind.novelty, 0)
elif (varian == "NS+Fit+BDDistP"):
ind.fitness.values = (ind.novelty, ind.fit, 0)
else:
print("WARNING: unknown variant: " + variant)
ind.fitness.values = ind.fit
else:
ind.fitness.values = ind.fit
# if it is not a multi-objective experiment, the select tool from DEAP
# has been configured above to take the right attribute into account
# and the fitness.values is thus ignored
gen = 0
# Do we look at the evolvability of individuals (WARNING: it will make runs much longer !)
generate_evolvability_samples(params, population, gen, toolbox)
record = params["stats"].compile(
population) if params["stats"] is not None else {}
record_offspring = params["stats_offspring"].compile(
population) if params["stats_offspring"] is not None else {}
logbook.record(gen=0,
nevals=len(invalid_ind),
**record,
**record_offspring)
if (verbosity(params)):
print(logbook.stream)
#generate_dumps(params, population, None, gen, pop1label="population", archive=None, logbook=None)
for ind in population:
ind.evolvability_samples = None # To avoid memory to inflate too much..
# Begin the generational process
for gen in range(1, params["nb_gen"] + 1):
if (gen == params["restart"]):
print("Restart: we reinitialize the population")
offspring = toolbox.population(n=lambda_)
for ind in offspring:
ind.bd = None
ind.id = generate_uuid()
ind.parent_id = None
population = []
else:
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_,
params["cxpb"], params["mutpb"])
# 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)
nb_eval += len(invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fit = fit[0]
ind.fitness.values = fit[0]
ind.parent_bd = ind.bd
ind.parent_id = ind.id
ind.id = generate_uuid()
ind.bd = listify(fit[1])
for ind in population:
ind.am_parent = 1
for ind in offspring:
ind.am_parent = 0
pq = population + offspring
if (params["stop_archive_update"] == gen):
params["add_strategy"] = "none"
if (params["pop_for_novelty_estimation"] == 0):
pop_for_novelty_estimation = []
elif (params["freeze_pop"] == -1) or (gen <= params["freeze_pop"]):
pop_for_novelty_estimation = list(pq)
archive = updateNovelty(pq, offspring, archive, params,
pop_for_novelty_estimation)
alpha_shape = alphashape.alphashape(archive.all_bd, alphas)
isortednov = sorted(range(len(pq)),
key=lambda k: pq[k].novelty,
reverse=True)
for i, ind in enumerate(pq):
ind.dist_to_explored_area = dist_to_shapes(ind.bd, alpha_shape)
ind.rank_novelty = isortednov.index(i)
#print("Indiv #%d: novelty=%f rank=%d"%(i, ind.novelty, ind.rank_novelty))
if (ind.parent_bd is None):
ind.dist_to_parent = 0
else:
ind.dist_to_parent = np.linalg.norm(
np.array(ind.bd) - np.array(ind.parent_bd))
if (emo):
if (varian == "NS+Fit"):
ind.fitness.values = (ind.novelty, ind.fit[0])
elif (varian == "NS+BDDistP"):
if (ind.parent_bd is None):
bddistp = 0
else:
bddistp = np.linalg.norm(
np.array(ind.bd) - np.array(ind.parent_bd))
ind.fitness.values = (ind.novelty, bddistp)
elif (varian == "NS+Fit+BDDistP"):
if (ind.parent_bd is None):
bddistp = 0
else:
bddistp = np.linalg.norm(
np.array(ind.bd) - np.array(ind.parent_bd))
ind.fitness.values = (ind.novelty, ind.fit, bddistp)
else:
print("WARNING: unknown variant: " + variant)
ind.fitness.values = ind.fit
else:
ind.fitness.values = ind.fit
if ((emo) and (offspring[0].fitness.values == offspring[0].fit)):
print("WARNING: EMO and the fitness is just the fitness !")
if (verbosity(params)):
print("Gen %d" % (gen))
else:
if (gen % 100 == 0):
print(" %d " % (gen), end='', flush=True)
elif (gen % 10 == 0):
print("+", end='', flush=True)
else:
print(".", end='', flush=True)
# Select the next generation population
if ("," in variant):
population[:] = toolbox.select(offspring, params["pop_size"])
else:
population[:] = toolbox.select(pq, params["pop_size"])
if (("eval_budget" in params.keys()) and (params["eval_budget"] != -1)
and (nb_eval >= params["eval_budget"])):
params["nb_gen"] = gen
terminates = True
else:
terminates = False
dump_data(population,
gen,
params,
prefix="population",
attrs=[
"all", "dist_to_explored_area", "dist_to_parent",
"rank_novelty"
],
force=terminates)
dump_data(population,
gen,
params,
prefix="bd",
complementary_name="population",
attrs=["bd"],
force=terminates)
dump_data(offspring,
gen,
params,
prefix="bd",
complementary_name="offspring",
attrs=["bd"],
force=terminates)
dump_data(archive.get_content_as_list(),
gen,
params,
prefix="archive",
attrs=["all"],
force=terminates)
generate_evolvability_samples(params, population, gen, toolbox)
# Update the statistics with the new population
record = params["stats"].compile(
population) if params["stats"] is not None else {}
record_offspring = params["stats_offspring"].compile(
offspring) if params["stats_offspring"] is not None else {}
logbook.record(gen=gen,
nevals=len(invalid_ind),
**record,
**record_offspring)
if (verbosity(params)):
print(logbook.stream)
for ind in population:
ind.evolvability_samples = None
if (terminates):
break
return population, archive, logbook, nb_eval
def eaMuCommaLambda(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
assert lambda_ >= mu, "lambda must be greater or equal to mu."
life_mean = 0
logbook = tools.Logbook()
logbook.header = ['gen', 'nevals', 'life_avg'
] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
eval = list(toolbox.map(toolbox.evaluate, invalid_ind))
fitnesses = [e[0] for e in eval]
life_list = [e[1] for e in eval]
life_mean = np.mean(life_list)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0,
nevals=len(invalid_ind),
life_avg=life_mean,
**record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
eval = list(toolbox.map(toolbox.evaluate, invalid_ind))
fitnesses = [e[0] for e in eval]
life_list = [e[1] for e in eval]
life_mean = np.mean(life_list)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen,
nevals=len(invalid_ind),
life_avg=life_mean,
**record)
if verbose:
print(logbook.stream)
return population, logbook
def evolve(self,evalfunc="q2loo"):
toolbox.register("genind", self.mkeindseed, self.indsize)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.genind)
toolbox.register("population",tools.initRepeat, list, toolbox.individual, n=self.popsize)
if evalfunc=="q2loo":
toolbox.register("evaluate", self.evalq2loo)
elif evalfunc=="q2lmo":
toolbox.register("evaluate", self.evalq2lmo)
elif evalfunc=="r2":
toolbox.register("evaluate", self.evalr2)
elif evalfunc=="r2adj":
toolbox.register("evaluate", self.evalr2adj)
else:
raise ValueError("not a valid evaluation function specified; use evalr2adj, evalr2, or q2loo")
toolbox.register("mate", tools.cxOnePoint) #Uniform, indpb=0.5)
toolbox.register("mutate", self.mutaRan)#, indpb=self.mut)
toolbox.register("select", tools.selBest)
#progress bar start!
#print 'Starting... # GEN FINISHED:',
origpop=toolbox.population()
#self.mkeindseed.count=0
population=cp.deepcopy(origpop)
fits=toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits,population):
ind.fitness.values=fit
avgfitnesses=[]
popfits=0
prb=ProgressBar(self.ngen)#, popfits)
#ProgressBar.score=0
#prb.animate()#popfits)
#prb.animate(popfits)
#steps=self.ngen/10
for gen in range(self.ngen):
try:
offspring=algorithms.varOr(population, toolbox, lambda_=self.popsize, cxpb=self.cx, mutpb=self.mut)
for ind in offspring:
ind.fitness.values=toolbox.evaluate(ind)
population=toolbox.select([k for k,v in itert.groupby(sorted(offspring+population))], k=100)
popfits = toolbox.map(toolbox.evaluate, population)
prb.animate(gen)
#prb.score=np.mean(popfits)
#ProgressBar.score=property(lambda self: self.score+np.mean(popfits))
#prb.update_time(1, prb.score)
except (KeyboardInterrupt, SystemExit):
return [origpop, toolbox.map(toolbox.evaluate, origpop), population, toolbox.map(toolbox.evaluate, population)]
except:
return [origpop, toolbox.map(toolbox.evaluate, origpop), population, toolbox.map(toolbox.evaluate, population)]
#print prb.score
#new progressbar try
#if gen%steps ==0:
# print '\b',gen, np.round(np.mean(popfits), decimals=3),
# sys.stdout.flush()
#print '\b'*1,
#print '\b Done!',
#sys.stdout.flush()
#print "Done!"
return [origpop, toolbox.map(toolbox.evaluate, origpop), population, toolbox.map(toolbox.evaluate, population)]
print "Done!"
def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
resetPeaks()
div=[]
logbook = tools.Logbook()
logbook2 = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
logbook2.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]
hofPop=toolbox.map(fitnessCustom,population) #change depending on eval func
for ind, fit in zip(population, hofPop):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats is not None else {}
div.append(momOfInertia(population))
logbook2.record(gen=0, nevals=len(population), **record)
fitnesses = []
for i in xrange(len(population)):
fitnesses.append(toolbox.evaluate((population)[i],population))
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
record = stats.compile(population) if stats is not None else {}
div.append(momOfInertia(population))
#div.append(averageHammingDistance(population))
logbook.record(gen=0, nevals=len(population), **record)
if verbose:
print logbook.stream
offline=[]
offlinegen=[]
accuracy=[]
leaps=0
#stability=[0]
#needed for accuracy
maxt=100 #highest peak
mint=0
# Begin the generational process
for gen in range(1, ngen+1):
#move the peaks
#poppy = sorted(population, key=attrgetter("fitness"),reverse=True)
#print poppy[0], poppy[0].fitness.values
if gen==150 or gen == 300:
print("Best individual is: %s\nwith fitness: %s" % (halloffame[0], halloffame[0].fitness))
halloffame.clear()
#print population
#clear every time peaks move as new fitness period
global peaks
#peaks[0]=bitFlip(peaks[0],0.1)
#peaks[2]=bitFlip(peaks[2],0.1)
print population
if gen==150:
peaks[1]=80
peaks[3]=100
if gen==300:
peaks[1]=100
peaks[3]=80
#peaks[0] = bitFlip(peaks[0],0.2)
#print peaks[0]
#print("Best individual is: %s\nwith fitness: %s" % (halloffame[0], halloffame[0].fitness))
for x in xrange(len(population)):
del population[x].fitness.values
fitnesses = []#toolbox.map(toolbox.evaluate, population+offspring)
for i in xrange(len(population)):
fitnesses.append(toolbox.evaluate((population)[i],population))
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
fitnesses = []#toolbox.map(toolbox.evaluate, population+offspring)
for i in xrange(len(population+offspring)):
fitnesses.append(toolbox.evaluate((population+offspring)[i],population+offspring))
for ind, fit in zip(population+offspring, fitnesses):
ind.fitness.values = fit
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population), **record)
if verbose:
print logbook.stream
# Update the hall of fame with the generated individuals fitness not dependant on sharing
hofPop = deepcopy(population)
fitnesses=toolbox.map(fitnessCustom,hofPop) #CHANGE EVAL FUNCTION HERE
for ind, fit in zip(hofPop, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
try:
a=halloffame[0].fitness.values
except IndexError:
a=0
halloffame.update(hofPop)
b=halloffame[0].fitness.values
if b>a:
leaps+=1
#record stats and offline and diversity and accuracy
record = stats.compile(hofPop) if stats is not None else {}
div.append(momOfInertia(hofPop))
logbook2.record(gen=gen, nevals=len(hofPop), **record)
print logbook2.stream
offline.append(halloffame[0].fitness.values[0])
offlinegen.append(gen)
accuracy.append((record["max"]-mint)/(maxt-mint))
#show graphs at final generation
if gen==ngen:
off=open("offline.txt","a")
bog=open("bog.txt","a")
mini=open("min.txt","a")
aver=open("aver.txt","a")
maxi=open("max.txt","a")
diver=open("div.txt","a")
print leaps
print "offline performance: ",sum(offline)/len(offline)
print "average bog: ", sum(logbook2.select("max"))/len(logbook2.select("max"))
#print offline
off.write(str(sum(offline)/len(offline)))
off.write(",")
bog.write(str(sum(logbook2.select("max"))/len(logbook2.select("max"))))
bog.write(",")
diver.write(str(div))
diver.write(",")
mini.write(str(logbook2.select("min")))
mini.write(",")
aver.write(str(logbook2.select("avg")))
aver.write(",")
maxi.write(str(logbook2.select("max")))
maxi.write(",")
#accuracy
plt.figure(2)
plt.title("accuracy")
plt.plot(offlinegen,accuracy)
plt.axis([0,ngen,0,1])
plt.show()
#diversity
plt.figure(3)
plt.title("Diversity")
plt.plot(xrange(len(div)),div)
plt.axis([0,len(div),0,1250])
plt.show()
#min av max
plt.figure(4)
plt.title("min, avg, max")
plt.plot(xrange(ngen+1),logbook2.select("min"),label="min")
plt.plot(xrange(ngen+1),logbook2.select("avg"),label="avg")
plt.plot(xrange(ngen+1),logbook2.select("max"),label="max")
plt.legend(loc=8)
plt.axis([0,ngen,0,maxt])
plt.show()
#stability
plt.figure(5)
plt.title("Stability")
stability=[]
for i in xrange(len(accuracy)):
stability.append(max(0,accuracy[i]-accuracy[i-1]))
plt.plot(offlinegen,stability)
plt.axis([0,ngen,0,1])
plt.show()
off.close()
bog.close()
mini.close()
aver.close()
maxi.close()
diver.close()
return population, logbook
def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
resetPeaks()
div=[]
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)
record = stats.compile(population) if stats is not None else {}
div.append(momOfInertia(population))
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print logbook.stream
offline=[]
offlinegen=[]
accuracy=[]
#leaps=0
#needed for accuracy
maxt=100 #highest peak
mint=0
# Begin the generational process
for gen in range(1, ngen+1):
#print peaks
if gen==150 or gen ==300:
halloffame.clear()
global peaks
#peaks[0] = bitFlip(peaks[0],0.1)
#peaks[2] = bitFlip(peaks[2],0.1)
print population
if gen==150:
peaks[1]=80
peaks[3]=100
if gen==300:
peaks[1]=100
peaks[3]=80
#print peaks
for x in xrange(len(population)):
del population[x].fitness.values
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
div.append(momOfInertia(population))
offline.append(halloffame[0].fitness.values[0])
offlinegen.append(gen)
accuracy.append((record["max"]-mint)/(maxt-mint))
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print logbook.stream
#show graphs at final generation
if gen==ngen:
#print leaps
#off=open("offline.txt","a")
#bog=open("bog.txt","a")
#mini=open("min.txt","a")
#aver=open("aver.txt","a")
#maxi=open("max.txt","a")
#diver=open("div.txt","a")
print "offline performance: ",sum(offline)/len(offline)
#off.write(str(sum(offline)/len(offline)))
#off.write(",")
#off.write("\n")
print "average bog: ", sum(logbook.select("max"))/len(logbook.select("max"))
#bog.write(str(sum(logbook.select("max"))/len(logbook.select("max"))))
#bog.write(",")
#bog.write("\n")
#accuracy
plt.figure(2)
plt.title("accuracy")
plt.plot(offlinegen,accuracy)
plt.axis([0,ngen,0,1])
plt.show()
#diversity
#diver.write(str(div))
#diver.write(",")
plt.figure(3)
plt.title("Diversity")
plt.plot(xrange(len(div)),div)
#plt.axis([0,len(div),0,maxt])
plt.show()
#min av max
#mini.write(str(logbook.select("min")))
#mini.write(",")
#mini.write("\n")
#aver.write(str(logbook.select("avg")))
#aver.write(",")
#aver.write("\n")
#maxi.write(str(logbook.select("max")))
#maxi.write(",")
#maxi.write("\n")
#print logbook.select("min")
#print " "
#print logbook.select("avg")
#print " "
#print logbook.select("max")
plt.figure(4)
plt.title("min, avg, max")
plt.plot(xrange(ngen+1),logbook.select("min"),label="min")
plt.plot(xrange(ngen+1),logbook.select("avg"),label="avg")
plt.plot(xrange(ngen+1),logbook.select("max"),label="max")
plt.legend(loc=8)
plt.axis([0,ngen,0,maxt])
plt.show()
#stability
#plt.figure(5)
#plt.title("Stability")
#stability=[]
#for i in xrange(len(accuracy)):
# stability.append(max(0,accuracy[i]-accuracy[i-1]))
#plt.plot(offlinegen,stability)
#plt.axis([0,ngen,0,1])
#plt.show()
#off.close()
#bog.close()
#mini.close()
#aver.close()
#maxi.close()
#diver.close()
return population, logbook
def ask(self):
self.offspring = varOr(self.population, self.toolbox,
self.configuration.lambda_,
1 - self.configuration.mutpb,
self.configuration.mutpb)
return self.offspring
def optimize(population,
toolbox,
mu,
lambda_,
net,
X,
y,
X_val,
y_val,
X_test,
y_test,
rated,
njobs,
path_group,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
assert lambda_ >= mu, "lambda must be greater or equal to mu."
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
# fitnesses=[]
# for individual in invalid_ind:
# m=rbf_optim(individual, X, y, X_val, y_val, X_test, y_test)
# fitnesses.append(m)
ncpus = joblib.load(os.path.join(path_group, 'total_cpus.pickle'))
gpu_status = joblib.load(os.path.join(path_group, 'gpu_status.pickle'))
njobs = int(ncpus - gpu_status)
cpu_status = njobs
joblib.dump(cpu_status, os.path.join(path_group, 'cpu_status.pickle'))
pool = mp.Pool(njobs)
results = [
pool.apply_async(rbf_optim,
args=(np.array(individual).ravel(), net, X, y, X_val,
y_val, X_test, y_test, rated))
for individual in invalid_ind
]
fitnesses = [p.get() for p in results]
pool.close()
pool.terminate()
pool.join()
# fitnesses = Parallel(n_jobs=njobs)(delayed(rbf_optim)(np.array(individual).ravel(), net, X, y, X_val, y_val, X_test, y_test, rated) for
# individual in invalid_ind)
# 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)
logbook = tools.Logbook()
# Gather all the fitnesses in one list and compute the stats
fits = np.array([ind.fitness.values for ind in population])
maximums = np.nanmax(fits, axis=0)
minimums = np.nanmin(fits, axis=0)
logbook.header = ['gen', 'nevals'
] + ['Max_mae:', 'Min_mae:', 'Max_rms:', 'Min_rms:']
record = {
'Max_mae:': maximums[0],
'Min_mae:': minimums[0],
'Max_rms:': maximums[2],
'Min_rms:': minimums[2]
}
print('GA rbf running generation 0')
print(record)
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
with elapsed_timer() as eval_elapsed:
for gen in range(1, ngen + 1):
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb,
mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
ncpus = joblib.load(os.path.join(path_group, 'total_cpus.pickle'))
gpu_status = joblib.load(
os.path.join(path_group, 'gpu_status.pickle'))
njobs = int(ncpus - gpu_status)
cpu_status = njobs
joblib.dump(cpu_status,
os.path.join(path_group, 'cpu_status.pickle'))
pool = mp.Pool(njobs)
results = [
pool.apply_async(rbf_optim,
args=(np.array(individual).ravel(), net, X, y,
X_val, y_val, X_test, y_test, rated))
for individual in invalid_ind
]
fitnesses = [p.get() for p in results]
pool.close()
pool.terminate()
pool.join()
# fitnesses = Parallel(n_jobs=njobs)(
# delayed(rbf_optim)(np.array(individual).ravel(), net, X, y, X_val, y_val, X_test, y_test, rated) for
# individual in invalid_ind)
# fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
fits = np.array([ind.fitness.values for ind in population])
maximums = np.nanmax(fits, axis=0)
minimums = np.nanmin(fits, axis=0)
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(offspring, mu)
# Update the statistics with the new population
record = {
'Max_mae:': maximums[0],
'Min_mae:': minimums[0],
'Max_rms:': maximums[2],
'Min_rms:': minimums[2]
}
print('GA rbf running generation ', str(gen))
print(record)
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
if eval_elapsed() > 1800:
break
return population, logbook
creator.Individual,
toolbox.bit,
n=num_connections)
toolbox.register('population',
tools.initRepeat,
list,
toolbox.individual,
n=population_size)
toolbox.register('evaluate', evaluate_fitness)
toolbox.register('mate', tools.cxUniform, indpb=0.1)
toolbox.register('mutate', tools.mutFlipBit, indpb=0.05)
toolbox.register('select', tools.selNSGA2)
population = toolbox.population()
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = fit
for gen in range(num_generations):
offspring = algorithms.varOr(population,
toolbox,
lambda_=population_size,
cxpb=0.5,
mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring + population, k=population_size)
print('Generation =', gen, 'Best Fitness =',
min([ind.fitness.values for ind in population]))
import knn, random
from deap import algorithms, base, creator, tools
def evalFitness(individual):
return knn.classification_rate(features=individual), sum(individual)
creator.create("FitnessMulti", base.Fitness, weights=(1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMulti)
toolbox = base.Toolbox()
toolbox.register("bit", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.bit, n=13)
toolbox.register("population", tools.initRepeat, list, toolbox.individual, n=100)
toolbox.register("evaluate", evalFitness)
toolbox.register("mate", tools.cxUniform, indpb=0.1)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selNSGA2)
population = toolbox.population()
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
ind.fitness.values = fit
for gen in range(50):
offspring = algorithms.varOr(population, toolbox, lambda_=100, cxpb=0.5,mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring + population, k=100)
def run(self,
X,
y,
X_test,
y_test,
scale_y,
mfs,
mu,
lambda_,
cxpb=0.6,
mutpb=0.4,
ngen=300):
perf = np.inf
front_best = None
rules = copy.deepcopy(self.rules)
self.population = self.toolbox.population()
param_ind = creator.Individual(self.x)
self.population.pop()
self.population.insert(len(self.population), param_ind)
i = 0
while i < 0.5 * len(self.population):
param_ind = mut_fun(self.x, 0, self.sigma, 0.8, self.lower_bound,
self.upper_bound, 0.6)
param_ind = creator.Individual(param_ind[0])
self.population.pop(i)
self.population.insert(i, param_ind)
i += 1
assert lambda_ >= mu, "lambda must be greater or equal to mu."
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in self.population if not ind.fitness.valid]
rules = copy.deepcopy(self.rules)
fit1 = evaluate(
np.array(invalid_ind[-1]).ravel(), X, y, X_test, y_test, scale_y,
self.rated, mfs, rules, self.p, self.resampling, self.num_samples,
self.n_ratio)
print('initial candidate error ', fit1[1])
rules = copy.deepcopy(self.rules)
fitnesses = Parallel(n_jobs=self.njobs)(
delayed(evaluate)(np.array(individual).ravel(), X, y, X_test,
y_test, scale_y, self.rated, mfs, rules, self.p,
self.resampling, self.num_samples, self.n_ratio)
for individual in invalid_ind)
# fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if self.hof is not None:
self.hof.update(self.population)
self.logbook = tools.Logbook()
# Gather all the fitnesses in one list and compute the stats
fits = np.array([ind.fitness.values for ind in self.population])
maximums = np.nanmax(fits, axis=0)
minimums = np.nanmin(fits, axis=0)
self.logbook.header = ['gen', 'nevals'] + [
'Max_sse:', 'Min_sse:', 'Max_mae:', 'Min_mae:'
]
self.logger.info('Iter: %s, Max_sse: %s, Min_mae: %s', 0, *minimums)
record = {
'Max_sse:': maximums[0],
'Min_sse:': minimums[0],
'Max_mae:': maximums[1],
'Min_mae:': minimums[1]
}
print('GA rbf running generation 0')
print(record)
self.logbook.record(gen=0, nevals=len(invalid_ind), **record)
print(self.logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Vary the population
rules = copy.deepcopy(self.rules)
offspring = algorithms.varOr(self.population, self.toolbox,
lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = Parallel(n_jobs=self.njobs)(delayed(evaluate)(
np.array(individual).ravel(), X, y, X_test, y_test, scale_y,
self.rated, mfs, rules, self.p, self.resampling,
self.num_samples, self.n_ratio) for individual in invalid_ind)
# fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
fits = np.array([ind.fitness.values for ind in self.population])
maximums = np.nanmax(fits, axis=0)
minimums = np.nanmin(fits, axis=0)
# Update the hall of fame with the generated individuals
if self.hof is not None:
self.hof.update(self.population)
# Select the next generation population
self.population[:] = self.toolbox.select(offspring, mu)
# Update the statistics with the new population
record = {
'Max_sse:': maximums[0],
'Min_sse:': minimums[0],
'Max_mae:': maximums[1],
'Min_mae:': minimums[1]
}
print('GA rbf running generation ', str(gen))
print(record)
self.logbook.record(gen=gen, nevals=len(invalid_ind), **record)
front = self.population
for i in range(len(front)):
if front[i].fitness.getValues()[0] < perf:
front_best = front[i]
perf = front[i].fitness.getValues()[0]
self.logger.info('Iter: %s, Max_sse: %s, Min_mae: %s', str(gen),
*minimums)
self.fmodel = self.evaluate(
np.array(front_best).ravel(), X, y, X_test, y_test, scale_y,
self.rated, mfs, self.rules, self.p, self.resampling)
def main():
# reset bookkeeping
System.resetBookKeeping()
## use existing bookkeeping data
#bookkeeping = np.load('bookkeeping.npz')
manager = Manager()
System.bookkeeping = manager.dict(System.bookkeeping)
pool = Pool(processes=num_processes)
toolbox.register("map", pool.map)
#System.bookkeeping = dict(System.bookkeeping)
#toolbox.register("map", map)
print "MULTIOBJECTIVE OPTIMIZATION: parallel version"
start_delta_time = time.time()
# optimization
random.seed(64)
logbook = tools.Logbook()
logbook.header = ["gen", "evals", "nfront", "mean1", "mean2", "tol1", "tol2", "time"]
pop = toolbox.population(n=NPOP)
fits = toolbox.map(toolbox.evaluate, pop)
for fit,ind in zip(fits, pop):
ind.fitness.values = fit
nevals = NPOP
g = 1
distances1 = []
distances2 = []
frontfitlast = np.array([[1., 0.]])
nevalsum = 0
evolStop = False
halloffame = tools.ParetoFront()
while not evolStop:
offspring = algorithms.varOr(pop, toolbox, lambda_=NPOP, cxpb=0.9, mutpb=0.1)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
nevals = len(invalid_ind)
nevalsum += nevals
fits = toolbox.map(toolbox.evaluate, invalid_ind)
for fit,ind in zip(fits, invalid_ind):
ind.fitness.values = fit
pop = toolbox.select(offspring+pop, k=NPOP)
front = toolbox.sort(offspring+pop, k=NPOP, first_front_only=True)[0]
halloffame.update(front)
## check if stop evolution
#distance=[]
#frontfit = [ind.fitness.values for ind in halloffame]
#for obj in frontfit:
#vector = np.array(frontfitlast)-np.array(obj)
#distance.append(min(np.linalg.norm(vector, axis=1)))
#distances.append(np.mean(distance))
#longest = 0.
#distances.append(np.mean(distance))
#vertlongest = 0.
#horilongest = 0.
#for point1 in frontfit:
#for point2 in frontfit:
##dist = np.linalg.norm(np.array(point1)-np.array(point2))
#if dist > longest:
#longest = dist
#tol = longest/NPOP
#tol = np.maximum(tol, TOL)
#evolStop = (len(distances)>NGEN and all(np.array(distances[-NGEN:])<tol)) or g>NMAX
#frontfitlast = frontfit
# check if stop evolution
distance1=[]
distance2=[]
frontfit = np.array([ind.fitness.values for ind in halloffame])
frontfit[frontfit[:,1] == 0,1] = 1.
frontfitlast[frontfitlast[:,1] == 0,1] = 1.
for obj in frontfit:
#vector = np.array(frontfitlast)-np.array(obj)
#distance.append(min(np.linalg.norm(vector, axis=1)))
distance1.append(min(np.abs(np.log10(frontfitlast[:,0])-np.log10(obj[0]))))
#distance2.append(min(np.abs(frontfitlast[:,1]-obj[1])))
distance2.append(min(np.abs(np.log10(frontfitlast[:,1])-np.log10(obj[1]))))
distances1.append(np.mean(distance1))
distances2.append(np.mean(distance2))
longest1 = 0.
longest2 = 0.
for point1 in frontfit:
for point2 in frontfit:
dist1 = np.abs(np.log10(point1[0])-np.log10(point2[0]))
#dist2 = np.abs(point1[1]-point2[1])
dist2 = np.abs(np.log10(point1[1])-np.log10(point2[1]))
if dist1 > longest1:
longest1 = dist1
if dist2 > longest2:
longest2 = dist2
tol1 = np.maximum(longest1/NPOP,TOL1)
tol2 = np.maximum(longest2/NPOP,TOL2)
evolStop = (len(distances1)>NGEN and all(np.array(distances1[-NGEN:])<tol1)
and all(np.array(distances2[-NGEN:])<tol2)) or g>NMAX
frontfitlast = frontfit
# Gather all the fitnesses in one list and print the stats
delta_time = time.time() - start_delta_time
logbook.record(gen=g, evals=nevals,nfront=len(halloffame),
mean1=distances1[-1], mean2=distances2[-1],
tol1=tol1, tol2=tol2, time=delta_time)
print(logbook.stream)
g+=1
pool.close()
pool.join()
System.bookkeeping = System.bookkeeping.copy()
delta_time = time.time() - start_delta_time
print 'DONE: {} s'.format(str(datetime.timedelta(seconds=delta_time)))
return pop, logbook, halloffame, nevalsum
def eaMuCommaLambda(cls,
population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None):
''' Method has been copied from DEAP framework in order to add some additional features (like adding verbosity
and a method which collect additional data related with simulations).
If you want to understand meaning of arguments please see usage of this method in "Execute" and read about
algorithm attributes in class description.
'''
assert lambda_ >= mu, "lambda must be greater or equal to mu."
# Create new logbook
cls.logbook = tools.Logbook()
cls.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
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
cls.logbook.record(gen=0, nevals=len(invalid_ind), **record)
cls.listOfPopulations = []
cls.listOfPopulations.append(copy.deepcopy(population))
# Begin the generational process
for gen in range(1, ngen + 1):
if cls.lverbose:
print("generation no:", gen)
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
population[:] = toolbox.select(offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
cls.logbook.record(gen=gen, nevals=len(invalid_ind), **record)
cls.listOfPopulations.append(copy.deepcopy(population))
# Get final population
cls.finalPopulation = population
def checkpoint_eaMuPlusLambda(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
logbook,
start_gen=0,
stats=None,
halloffame=None,
FREQ=DEFAULT_CHECKPOINTFREQ,
checkpoint=None,
CPOUT=None,
verbose=__debug__,
timelimit=None,
termination=None):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda\_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:param ngen: The number of generation.
:param stats: A :class:`~deap.tools.Statistics` object that is updated
inplace, optional.
:param halloffame: A :class:`~deap.tools.HallOfFame` object that will
contain the best individuals, optional.
:param verbose: Whether or not to log the statistics.
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution.
The algorithm takes in a population and evolves it in place using the
:func:`varOr` function. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
logbook will contain the generation number, the number of evalutions for
each generation and the statistics if a :class:`~deap.tools.Statistics` is
given as argument. The *cxpb* and *mutpb* arguments are passed to the
:func:`varOr` function. The pseudocode goes as follow ::
evaluate(population)
for g in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(population + offspring, mu)
First, the individuals having an invalid fitness are evaluated. Second,
the evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by the :func:`varOr` function. The
offspring are then evaluated and the next generation population is
selected from both the offspring **and** the population. Finally, when
*ngen* generations are done, the algorithm returns a tuple with the final
population and a :class:`~deap.tools.Logbook` of the evolution.
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
if timelimit:
terminator = TimeTerminator(limit=timelimit)
else:
terminator = GenerationTerminator(limit=ngen)
if checkpoint:
# Removed feature
print "Resuming from generation", start_gen
print logbook.stream
else:
logbook = tools.Logbook()
logbook.header = ['gen', 'evals', 'memoize', "maxdepth"] + (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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=start_gen, evals=len(invalid_ind), memoize=0, maxdepth=numpy.max([x.height for x in population]), **record)
if verbose:
print logbook.stream
if len(population) == 1:
population.append(population[0])
if termination == "auto":
#TODO: Place somewhere better
terminator = MOEATerminationDetection()
# Begin the generational process
gen = start_gen
while not terminator.done():
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
# Update termination conditions
gen+=1
if termination == "auto":
P = [list(y) for y in [x.fitness.values for x in halloffame[:]]]
halloffame.update(offspring)
Q = [list(y) for y in [x.fitness.values for x in halloffame[:]]]
terminator.update(P,Q, gen-1)
else:
halloffame.update(offspring)
terminator.update(gen=gen)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, evals=len(invalid_ind), memoize=toolbox.memoizecount(), maxdepth=numpy.max([x.height for x in population]), **record )
if verbose: print logbook.stream
# Store checkpoint if gen is valid
if CPOUT:
continue # Checkpoints disabled
this_checkpoint = CPOUT+"/"+str(gen)+".checkpoint"
check_store_checkpoint(this_checkpoint,
population,
gen,
halloffame,
logbook,
toolbox,
rndstate=random.getstate(),
numpystate=numpy.random.get_state(),
pset=None
)
if CPOUT :
this_checkpoint = CPOUT+"/"+str(ngen)+".checkpoint"
store_checkpoint(this_checkpoint,
population,
ngen,
halloffame,
logbook,
toolbox,
rndstate=random.getstate(),
numpystate=numpy.random.get_state(),
pset=None
)
return population, logbook
def main():
## reset bookkeeping
#Component.resetCostKeeping()
#Component.resetPfKeeping()
#Component.resetRiskKeeping()
# use existing pf data
suffix = rate2suffix(icorr_mean_list)
pfname = 'pfkeeping_'+suffix+'.npz'
costname = 'costkeeping_'+suffix+'.npz'
datapath = os.path.join(os.path.abspath('./'), 'data')
pffile = os.path.join(datapath,pfname)
costfile = os.path.join(datapath,costname)
pfkeeping = np.load(pffile)
Component.pfkeeping['flexure'] = pfkeeping['flexure']
Component.pfkeeping['shear'] = pfkeeping['shear']
Component.pfkeeping['deck'] = pfkeeping['deck']
# use existing cost data
costkeeping = np.load(costfile)
Component.costkeeping['flexure'] = costkeeping['flexure']
Component.costkeeping['shear'] = costkeeping['shear']
Component.costkeeping['deck'] = costkeeping['deck']
#manager = Manager()
#Component.pfkeeping = manager.dict(Component.pfkeeping)
#Component.costkeeping = manager.dict(Component.costkeeping)
#Component.riskkeeping = manager.dict(Component.riskkeeping)
#pool = Pool(processes=num_processes)
#toolbox.register("map", pool.map)
Component.pfkeeping = dict(Component.pfkeeping)
Component.costkeeping = dict(Component.costkeeping)
Component.riskkeeping = dict(Component.riskkeeping)
toolbox.register("map", map)
print "MULTIOBJECTIVE OPTIMIZATION: parallel version"
start_delta_time = time.time()
# optimization
#random.seed(64)
logbook = tools.Logbook()
logbook.header = ["gen", "evals", "nfront", "mean1", "mean2", "tol1", "tol2", "time"]
pop = toolbox.population(n=NPOP)
fits = toolbox.map(toolbox.evaluate, pop)
for fit,ind in zip(fits, pop):
ind.fitness.values = fit
nevals = NPOP
g = 1
distances1 = []
distances2 = []
frontfitlast = np.array([[1., 0.]])
nevalsum = 0
evolStop = False
halloffame = tools.ParetoFront()
while not evolStop:
offspring = algorithms.varOr(pop, toolbox, lambda_=NPOP, cxpb=0.9, mutpb=0.1)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
nevals = len(invalid_ind)
nevalsum += nevals
fits = toolbox.map(toolbox.evaluate, invalid_ind)
for fit,ind in zip(fits, invalid_ind):
ind.fitness.values = fit
pop = toolbox.select(offspring+pop, k=NPOP)
front = toolbox.sort(offspring+pop, k=NPOP, first_front_only=True)[0]
halloffame.update(front)
# check if stop evolution
distance1=[]
distance2=[]
frontfit = np.array([ind.fitness.values for ind in halloffame])
frontfit[frontfit[:,1] == 0,1] = 1.
frontfitlast[frontfitlast[:,1] == 0,1] = 1.
for obj in frontfit:
#vector = np.array(frontfitlast)-np.array(obj)
#distance.append(min(np.linalg.norm(vector, axis=1)))
distance1.append(min(np.abs(np.log10(frontfitlast[:,0])-np.log10(obj[0]))))
#distance2.append(min(np.abs(frontfitlast[:,1]-obj[1])))
distance2.append(min(np.abs(np.log10(frontfitlast[:,1])-np.log10(obj[1]))))
distances1.append(np.mean(distance1))
distances2.append(np.mean(distance2))
longest1 = 0.
longest2 = 0.
for point1 in frontfit:
for point2 in frontfit:
dist1 = np.abs(np.log10(point1[0])-np.log10(point2[0]))
#dist2 = np.abs(point1[1]-point2[1])
dist2 = np.abs(np.log10(point1[1])-np.log10(point2[1]))
if dist1 > longest1:
longest1 = dist1
if dist2 > longest2:
longest2 = dist2
tol1 = np.maximum(longest1/NPOP,TOL1)
tol2 = np.maximum(longest2/NPOP,TOL2)
evolStop = (len(distances1)>NGEN and all(np.array(distances1[-NGEN:])<tol1)
and all(np.array(distances2[-NGEN:])<tol2)) or g>NMAX
frontfitlast = frontfit
# Gather all the fitnesses in one list and print the stats
delta_time = time.time() - start_delta_time
logbook.record(gen=g, evals=nevals,nfront=len(halloffame),
mean1=distances1[-1], mean2=distances2[-1],
tol1=tol1, tol2=tol2, time=delta_time)
print(logbook.stream)
g+=1
#pool.close()
#pool.join()
delta_time = time.time() - start_delta_time
print 'DONE: {} s'.format(str(datetime.timedelta(seconds=delta_time)))
return pop, logbook, halloffame, nevalsum
def eaMuPlusLambda(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__,
graph=False):
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print logbook.stream
if graph:
graph, pop_series = graph_gen(refmap, population, target)
pbar = range(0, ngen) if verbose else trange(ngen, leave=False)
for gen in pbar:
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if graph:
update_series(graph, pop_series, population)
if verbose:
print logbook.stream
else:
desc = "pop: " + str(len(population)) + " gen: " + str(ngen)
pbar.set_description(desc)
return record, logbook
def eaMuPlusLambda(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
:param lambda\_: The number of children to produce at each generation.
:param cxpb: The probability that an offspring is produced by crossover.
:param mutpb: The probability that an offspring is produced by mutation.
:param ngen: The number of generation.
:param stats: A :class:`~deap.tools.Statistics` object that is updated
inplace, optional.
:param halloffame: A :class:`~deap.tools.HallOfFame` object that will
contain the best individuals, optional.
:param verbose: Whether or not to log the statistics.
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution.
The algorithm takes in a population and evolves it in place using the
:func:`varOr` function. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
logbook will contain the generation number, the number of evalutions for
each generation and the statistics if a :class:`~deap.tools.Statistics` is
given as argument. The *cxpb* and *mutpb* arguments are passed to the
:func:`varOr` function. The pseudocode goes as follow ::
evaluate(population)
for g in range(ngen):
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
evaluate(offspring)
population = select(population + offspring, mu)
First, the individuals having an invalid fitness are evaluated. Second,
the evolutionary loop begins by producing *lambda_* offspring from the
population, the offspring are generated by the :func:`varOr` function. The
offspring are then evaluated and the next generation population is
selected from both the offspring **and** the population. Finally, when
*ngen* generations are one, the algorithm returns a tuple with the final
population and a :class:`~deap.tools.Logbook` of the evolution.
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
variation.
"""
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
handler.setFormatter(logging.Formatter(fmt='%(message)s'))
# #old_handler = eaMuPlusLambda._log.handlers[0]
# # eaMuPlusLambda._log.removeHandler(old_handler)
# eaMuPlusLambda._log.addHandler(handler)
logger = logging.getLogger('algorithms')
logger.setLevel(logging.INFO)
logger.addHandler(handler)
if verbose:
# print(logbook.stream)
# eaMuPlusLambda._log.info(logbook.stream)
logger.info(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
try:
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
# print(logbook.stream)
# eaMuPlusLambda._log.info(logbook.stream)
logger.info(logbook.stream)
except KeyboardInterrupt:
# print('Interrupt evolution from the user... continue PKP!')
eaMuPlusLambda._log.warning(
'Interrupt evolution from the user... continue PKP!')
break
return population, logbook
def main(args):
run_date = time.time()
# Configure Logging
root = logging.getLogger()
if(args.debug):
root.setLevel(logging.DEBUG)
else:
root.setLevel(logging.INFO)
if args.quiet:
root.propogate = False
# Set up the database.
pgdb = DBUtils(config_file=DB_CONFIG_FILE)
# Get the name of this agent trail for later use
at = AgentTrail()
at.readTrail(args.trail, DB_CONFIG_FILE)
trail_name = at.getName()
if not args.quiet and not args.debug and not args.script_mode:
try:
TOTAL_GENERATIONS = (len(args.network) *
args.generations * args.repeat)
widgets = ['Processed: ', progressbar.Percentage(), ' ',
progressbar.Bar(marker=progressbar.RotatingMarker()),
' ', progressbar.ETA()]
pbar = progressbar.ProgressBar(
widgets=widgets,
maxval=TOTAL_GENERATIONS).start()
except:
pbar = None
else:
pbar = None
current_overall_gen = 0
for curr_network in args.network:
# Query the database to get the network information.
pybrain_network = pgdb.getNetworkByID(curr_network)
temp_f_h, temp_f_network = tempfile.mkstemp()
os.close(temp_f_h)
with open(temp_f_network, "w") as f:
pickle.dump(pybrain_network, f)
# TODO: Need to fix this for chemistry support here.
if "Chemical" in pybrain_network.name:
chem_re = re.compile(
"JL NN Chemical DL([0-9]+) \([0-9]+,[0-9]+,[0-9]+\) v[0-9]+")
chem_dl_length = int(chem_re.findall(pybrain_network.name)[0])
network_params_len = len(pybrain_network.params) + chem_dl_length * 3
else:
network_params_len = len(pybrain_network.params)
# Query the database to get the trail information.
(data_matrix,
db_trail_name,
init_rot) = pgdb.getTrailData(args.trail)
# Calculate the maximum amount of food for potential later comparison.
MAX_FOOD = np.bincount(np.array(data_matrix).flatten())[1]
for curr_repeat in range(0, args.repeat):
repeat_start_time = datetime.datetime.now()
gens_stat_list = [None] * args.generations
# Create an empty array to store the launches for SCOOP.
launches = []
# Prepare the array for storing hall of fame.
hof_array = np.zeros((args.generations,
network_params_len))
toolbox = base.Toolbox()
toolbox.register("map", scoop.futures.map)
toolbox.register("attr_float", random.uniform,
a=args.weight_min, b=args.weight_max)
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_float, n=network_params_len)
toolbox.register("population", tools.initRepeat, list,
toolbox.individual)
an_temp = AgentNetwork()
an_temp.readNetworkFromFile(temp_f_network)
at_temp = AgentTrail()
at_temp.readTrailInstant(data_matrix, db_trail_name, init_rot)
toolbox.register("evaluate", __singleMazeTask, moves=args.moves,
network=pickle.dumps(an_temp), trail=pickle.dumps(at_temp))
toolbox.register("mate", tools.cxTwoPoint)
if args.mutate_type == 1:
toolbox.register("mutate",
tools.mutFlipBit,
indpb=P_BIT_MUTATE)
elif args.mutate_type == 2:
toolbox.register("mutate",
mutUniformFloat,
low=args.weight_min,
up=args.weight_max,
indpb=P_BIT_MUTATE)
elif args.mutate_type == 3:
toolbox.register("mutate",
mutUniformFloat,
low=args.weight_min,
up=args.weight_max,
indpb=0.30)
elif args.mutate_type == 4:
toolbox.register("mutate",
mutUniformFloat,
low=args.weight_min,
up=args.weight_max,
indpb=0.10)
elif args.mutate_type == 5:
toolbox.register("mutate",
tools.mutGaussian,
mu=0,
indpb=0.05)
else:
print "ERROR: Please selct a valid mutate type!"
sys.exit(10)
if args.selection == 1:
# Selection is tournment. Must use argument from user.
toolbox.register("select", tools.selTournament,
tournsize=args.tournament_size)
elif args.selection == 2:
toolbox.register("select", tools.selRoulette)
elif args.selection == 3:
toolbox.register("select", tools.selNSGA2)
elif args.selection == 4:
toolbox.register("select", tools.selSPEA2)
elif args.selection == 5:
toolbox.register("select", tools.selRandom)
elif args.selection == 6:
toolbox.register("select", tools.selBest)
elif args.selection == 7:
toolbox.register("select", tools.selWorst)
elif args.selection == 8:
toolbox.register("select", tools.selTournamentDCD)
else:
print "ERROR: Something is wrong with selection method!"
sys.exit(10)
# Start a new evolution
population = toolbox.population(n=args.population)
halloffame = tools.HallOfFame(maxsize=1)
food_stats = tools.Statistics(key=lambda ind: ind.fitness.values[0])
move_stats = tools.Statistics(key=lambda ind: ind.fitness.values[1])
mstats = tools.MultiStatistics(food=food_stats, moves=move_stats)
mstats.register("min", np.min)
mstats.register("avg", np.mean)
mstats.register("max", np.max)
mstats.register("std", np.std)
mstats.register("mode", mode)
# Record the start of this run.
log_time = datetime.datetime.now()
# Evaluate and record the first generation here.
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
# Determine the current generations statistics.
record = mstats.compile(population)
if args.debug:
print "DEBUG: Completed generation 1"
hof_indiv = np.array(tools.selBest(population, k=1)[0])
hof_array[0] = hof_indiv
# Add the hall of fame to launches.
launches.append(
scoop.futures.submit(__singleMazeTask,
hof_indiv,
args.moves,
pickle.dumps(an_temp),
pickle.dumps(at_temp),
1,
record)
)
# Keep track of the average food history.
mean_food_history = []
smart_term_msg = ""
# Begin the generational process
for gen in range(2, args.generations + 1):
# Vary the pool of individuals
if args.variation in [1]:
offspring = algorithms.varAnd(population, toolbox,
cxpb=args.prob_crossover, mutpb=args.prob_mutate)
elif args.variation in [2, 3, 4]:
offspring = algorithms.varOr(population, toolbox,
lambda_=args.lambda_,
cxpb=args.prob_crossover, mutpb=args.prob_mutate)
elif args.variation in [5]:
# Take and modify the varAnd from DEAP.
offspring = [toolbox.clone(ind) for ind in population]
# Apply crossover and mutation on the offspring
for i in range(1, len(offspring), 2):
if random.random() < args.prob_crossover:
offspring[i-1], offspring[i] = toolbox.mate(
offspring[i-1], offspring[i])
del (offspring[i-1].fitness.values,
offspring[i].fitness.values)
for i in range(len(offspring)):
if random.random() < args.prob_mutate:
if args.mutate_type in [5]:
offspring[i], = toolbox.mutate(
offspring[i],
sigma=np.std(offspring[i]))
else:
offspring[i], = toolbox.mutate(
offspring[i], offspring[i])
del offspring[i].fitness.values
else:
print ("ERROR: Something is really wrong! " +
"Reached an invalid variation type!")
sys.exit(5)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Replace the current population by the offspring
if args.variation in [2, 3]:
population[:] = toolbox.select(offspring, args.population)
elif args.variation in [4, 5]:
population[:] = toolbox.select(offspring + population,
args.population)
else:
population[:] = offspring
# Determine the current generations statistics.
record = mstats.compile(population)
if args.debug:
print "DEBUG: Completed generation {0}.".format(gen)
print (
"DEBUG: Food (Min / Max / Avg / Std / Mode): "
"{0} / {1} / {2} / {3} / {4}".format(
record["food"]["min"],
record["food"]["max"],
record["food"]["avg"],
record["food"]["std"],
record["food"]["mode"]))
print (
"DEBUG: Moves (Min / Max / Avg / Std / Mode): "
"{0} / {1} / {2} / {3} / {4}".format(
record["moves"]["min"],
record["moves"]["max"],
record["moves"]["avg"],
record["moves"]["std"],
record["moves"]["mode"]))
hof_indiv = np.array(tools.selBest(population, k=1)[0])
hof_array[gen - 1] = hof_indiv
# Add the hall of fame to launches.
launches.append(
scoop.futures.submit(__singleMazeTask,
hof_indiv,
args.moves,
pickle.dumps(an_temp),
pickle.dumps(at_temp),
gen,
record)
)
# Update the mean food history.
mean_food_history.append(record["food"]["avg"])
# Update the progress bar
if pbar:
current_overall_gen += 1
pbar.update(current_overall_gen)
# Check if it is time to quit if variation is 3. Critera are
# any of the following:
# 1) All food has been collected.
# 2) Mean has not changed for args.mean_check_length
# 3) Run out of generations (happens without this if)
if args.variation in [3, 4, 5] and not args.no_early_quit:
if (int(record["food"]["max"]) == int(MAX_FOOD)):
smart_term_msg = ("Exited at generation {0} because "
"all food was consumed.").format(gen)
break
elif(len(mean_food_history) >= args.mean_check_length and
(np.std(mean_food_history[-args.mean_check_length:])
< 0.1)):
smart_term_msg = ("Exited at generation {0} because "
"mean check length has been met.").format(gen)
break
# Evaluate the Hall of Fame individual for each generation here
# in a multithreaded fashion to speed things up.
for this_future in scoop.futures.as_completed(launches):
result = this_future.result()
gens_stat_list[result[0] - 1] = result[1]
# Remove all of the None values from the gen_stat_list
gens_stat_list = filter(lambda a: a is not None, gens_stat_list)
# Record the statistics on this run.
run_info = {}
run_info["trails_id"] = args.trail
run_info["networks_id"] = curr_network
run_info["selection_id"] = args.selection
run_info["mutate_id"] = args.mutate_type
run_info["host_type_id"] = 1 # Only one host type for now.
run_info["variations_id"] = args.variation
run_info["run_date"] = log_time
run_info["hostname"] = socket.getfqdn()
run_info["generations"] = args.generations
run_info["population"] = args.population
run_info["moves_limit"] = args.moves
run_info["sel_tourn_size"] = args.tournament_size
if args.variation in [1, 5]:
run_info["lambda"] = 0
else:
run_info["lambda"] = args.lambda_
run_info["p_mutate"] = args.prob_mutate
run_info["p_crossover"] = args.prob_crossover
run_info["weight_min"] = args.weight_min
run_info["weight_max"] = args.weight_max
run_info["debug"] = args.debug
# Version for if anything changes in python GA Algorithm
run_info["algorithm_ver"] = 2
run_info["mean_check_length"] = args.mean_check_length
run_info["runtime"] = (datetime.datetime.now() -
repeat_start_time)
if not args.disable_db:
run_id = pgdb.recordRun(run_info, gens_stat_list)
else:
run_id = -1
if args.script_mode:
if run_id > 0:
print (
"Completed repeat {0} with run ID {1}. {2}".format(
curr_repeat,
run_id,
smart_term_msg
))
else:
print (
"Completed repeat {0} without logging to DB. {1}".format(
curr_repeat,
smart_term_msg
))
# Delete the temporary file
os.remove(temp_f_network)
# Calculate and display the total runtime
if pbar:
pbar.finish()
total_time_s = time.time() - run_date
if run_id > 0:
print "Final Run ID {0} completed all runs in {1}. {2}".format(
run_id,
time.strftime('%H:%M:%S', time.gmtime(total_time_s)),
smart_term_msg)
else:
print "UNLOGGED Run completed in {0}. {1}".format(
time.strftime('%H:%M:%S', time.gmtime(total_time_s)),
smart_term_msg)
def eaMuPlusLambda1(population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
global phase
global snakeList
global snakeList1
global succFlag
succFlag = False
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, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
#print(fit, ind.fitness.values)
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0,
nevals=len(population),
hof=0.0,
besthofgen=0,
**record)
if verbose:
print(logbook.stream)
#print(record)
k = 0.0
kk = 0
snakeList1[:] = []
# Begin the generational process
for gen in range(1, ngen + 1):
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
#invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len([]), hof=k, besthofgen=kk, **record)
if record['avgbest'] > k:
k = record['avgbest']
kk = gen
if gen - kk > 10:
return population, logbook
if verbose:
print(logbook.stream)
return population, logbook
def ea_mu_plus_lambda(population,
toolbox,
checkpoint,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
verbose=__debug__):
if checkpoint:
# A file name has been give, then load the data from the file
with open(checkpoint, "rb") as cp_file:
cp = pickle.load(cp_file)
population = cp["population"]
start_gen = cp["generation"] + 1
halloffame = cp["halloffame"]
logbook = cp["logbook"]
random.setstate(cp["rndstate"])
best_specimens = cp["bestspecimens"]
print(logbook)
else:
# start a new evolution since no cp_file was given
# population = toolbox.population(n=36)
start_gen = 1
# halloffame = tools.HallOfFame(maxsize=1)
logbook = tools.Logbook()
best_specimens = []
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)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(start_gen, ngen + 1):
# clear the directory of all sim files
clear_directory()
# Vary the population
offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
# 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
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Append the best solution from this gen to the best specimens
# array
#
best_specimens.append(tools.selBest(population, k=1)[0])
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
if gen % FREQ == 0:
# fill the dictionary
cp = dict(population=population,
generation=gen,
halloffame=halloffame,
logbook=logbook,
rndstate=random.getstate(),
bestspecimens=best_specimens)
with open(CHECK_POINT, "wb") as cp_file:
pickle.dump(cp, cp_file)
return population, logbook