def main():
NGEN = 100
MU, LAMBDA = 10, 10
verbose = True
# create_plot = True
population = []
for i in range(0, 10):
population.append(creator.Individual(*get_random_parameters()))
# print(population)
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
# hof = tools.HallOfFame(1)
strategy = cma.StrategyMultiObjective(population,
sigma=1.0,
mu=MU,
lambda_=LAMBDA)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
fitness_history = []
for gen in range(NGEN):
population = toolbox.generate()
# Evaluate
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
fitness_history.append(fit)
toolbox.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population), **record)
if verbose:
print(logbook.stream)
# print(population)
# print(hof[0])
# print(population, list(map(test_func, population)))
for ind in population:
acc, pr_time = test_func(ind)
print("ind: {0} accuracy: {1} time: {2}".format(ind, acc, pr_time))
def test_mo_cma_es():
def distance(feasible_ind, original_ind):
"""A distance function to the feasibility region."""
return sum((f - o)**2 for f, o in zip(feasible_ind, original_ind))
def closest_feasible(individual):
"""A function returning a valid individual from an invalid one."""
feasible_ind = numpy.array(individual)
feasible_ind = numpy.maximum(BOUND_LOW, feasible_ind)
feasible_ind = numpy.minimum(BOUND_UP, feasible_ind)
return feasible_ind
def valid(individual):
"""Determines if the individual is valid or not."""
if any(individual < BOUND_LOW) or any(individual > BOUND_UP):
return False
return True
NDIM = 5
BOUND_LOW, BOUND_UP = 0.0, 1.0
MU, LAMBDA = 10, 10
NGEN = 500
# The MO-CMA-ES algorithm takes a full population as argument
population = [creator.__dict__[INDCLSNAME](x) for x in numpy.random.uniform(BOUND_LOW, BOUND_UP, (MU, NDIM))]
toolbox = base.Toolbox()
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.decorate("evaluate", tools.ClosestValidPenality(valid, closest_feasible, 1.0e-6, distance))
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
strategy = cma.StrategyMultiObjective(population, sigma=1.0, mu=MU, lambda_=LAMBDA)
toolbox.register("generate", strategy.generate, creator.__dict__[INDCLSNAME])
toolbox.register("update", strategy.update)
for gen in range(NGEN):
# Generate a new population
population = toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Update the strategy with the evaluated individuals
toolbox.update(population)
hv = hypervolume(strategy.parents, [11.0, 11.0])
assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (hv, HV_THRESHOLD)
def test_mo_cma_es():
def distance(feasible_ind, original_ind):
"""A distance function to the feasibility region."""
return sum((f - o)**2 for f, o in zip(feasible_ind, original_ind))
def closest_feasible(individual):
"""A function returning a valid individual from an invalid one."""
feasible_ind = numpy.array(individual)
feasible_ind = numpy.maximum(BOUND_LOW, feasible_ind)
feasible_ind = numpy.minimum(BOUND_UP, feasible_ind)
return feasible_ind
def valid(individual):
"""Determines if the individual is valid or not."""
if any(individual < BOUND_LOW) or any(individual > BOUND_UP):
return False
return True
NDIM = 5
BOUND_LOW, BOUND_UP = 0.0, 1.0
MU, LAMBDA = 10, 10
NGEN = 500
numpy.random.seed(128)
# The MO-CMA-ES algorithm takes a full population as argument
population = [
creator.__dict__[INDCLSNAME](x)
for x in numpy.random.uniform(BOUND_LOW, BOUND_UP, (MU, NDIM))
]
toolbox = base.Toolbox()
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.decorate(
"evaluate",
tools.ClosestValidPenalty(valid, closest_feasible, 1.0e+6, distance))
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
strategy = cma.StrategyMultiObjective(population,
sigma=1.0,
mu=MU,
lambda_=LAMBDA)
toolbox.register("generate", strategy.generate,
creator.__dict__[INDCLSNAME])
toolbox.register("update", strategy.update)
for gen in range(NGEN):
# Generate a new population
population = toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Update the strategy with the evaluated individuals
toolbox.update(population)
# Note that we use a penalty to guide the search to feasible solutions,
# but there is no guarantee that individuals are valid.
# We expect the best individuals will be within bounds or very close.
num_valid = 0
for ind in strategy.parents:
dist = distance(closest_feasible(ind), ind)
if numpy.isclose(dist, 0.0, rtol=1.e-5, atol=1.e-5):
num_valid += 1
assert num_valid >= len(strategy.parents)
# Note that NGEN=500 is enough to get consistent hypervolume > 116,
# but not 119. More generations would help but would slow down testing.
hv = hypervolume(strategy.parents, [11.0, 11.0])
assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (
hv, HV_THRESHOLD)
def CMAES_MO(var,
weights,
funcs_l,
sigma,
verbose=True,
MAXITER=100,
STAGNATION_ITER=10,
lambda_=3,
mu=5):
NRESTARTS = 10 # Initialization + 9 I-POP restarts
creator.create("MaFitness", base.Fitness, weights=weights)
creator.create("Individual", list, fitness=creator.MaFitness)
toolbox = base.Toolbox()
eval_funcs = lambda x: tuple([f(x) for f in funcs_l])
toolbox.register("evaluate", eval_funcs)
S.Swarm.controller.rez_params()
S.model = var
c = S.extract_genotype()
init_func = lambda c, sigma, size: np.random.normal(c, sigma, size)
toolbox.register("attr_float", init_func, c, sigma, len(var))
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval_funcs)
halloffame = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: np.array([ind.fitness.values]))
stats.register("average", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
logbooks = list()
bestvalues = list()
medianvalues = list()
i = 0
t = 0
while i < (NRESTARTS):
pop = toolbox.population(n=mu)
strategy = cma.StrategyMultiObjective(centroid=c,
sigma=sigma,
lambda_=lambda_,
population=pop)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
logbooks.append(tools.Logbook())
logbooks[
-1].header = "gen", "evals", "restart", "regime", "std", "min", "avg", "max"
conditions = {
"MaxIter": False,
"TolHistFun": False,
"EqualFunVals": False,
"TolX": False,
"TolUpSigma": False,
"Stagnation": False,
"ConditionCov": False,
"NoEffectAxis": False,
"NoEffectCoor": False
}
while not any(conditions.values()):
if (t % 5 == 0):
S.Swarm.controller.visibility = True
else:
S.Swarm.controller.visibility = False
t = t + 1
# Generate a new population
population = pop + toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
halloffame.update(population)
record = stats.compile(population)
logbooks[-1].record(gen=t, restart=i, **record)
if verbose:
print(logbooks[-1].stream)
# Update the strategy with the evaluated individuals
toolbox.update(population)
# Log the best and median value of this population
bestvalues.append(population[-1].fitness.values)
medianvalues.append(population[int(round(len(population) /
2.))].fitness.values)
if t >= MAXITER:
# The maximum number of iteration per CMA-ES ran
conditions["MaxIter"] = True
if len(bestvalues) > STAGNATION_ITER and len(medianvalues) > STAGNATION_ITER and \
np.median(bestvalues[-20:]) >= np.median(
bestvalues[-STAGNATION_ITER:-STAGNATION_ITER + 20]) and \
np.median(medianvalues[-20:]) >= np.median(
medianvalues[-STAGNATION_ITER:-STAGNATION_ITER + 20]):
# Stagnation occured
conditions["Stagnation"] = True
pop = [p.fitness.values for p in population[-1:-mu]]
stop_causes = [k for k, v in conditions.items() if v]
print(
"Stopped because of condition%s %s" %
((":" if len(stop_causes) == 1 else "s:"), ",".join(stop_causes)))
i += 1
def main():
# The cma module uses the numpy random number generator
# numpy.random.seed(128)
MU, LAMBDA = 10, 10
NGEN = 500
verbose = True
# The MO-CMA-ES algorithm takes a full population as argument
population = [
creator.Individual(x) for x in (numpy.random.uniform(0, 1, (MU, N)))
]
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
strategy = cma.StrategyMultiObjective(population,
sigma=1.0,
mu=MU,
lambda_=LAMBDA)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
for gen in range(NGEN):
# Generate a new population
population = toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Update the strategy with the evaluated individuals
toolbox.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population), **record)
if verbose:
print(logbook.stream)
if verbose:
print("Final population hypervolume is %f" %
hypervolume(strategy.parents, [11.0, 11.0]))
# import matplotlib.pyplot as plt
# valid_front = numpy.array([ind.fitness.values for ind in strategy.parents if valid(ind)])
# invalid_front = numpy.array([ind.fitness.values for ind in strategy.parents if not valid(ind)])
# fig = plt.figure()
# if len(valid_front) > 0:
# plt.scatter(valid_front[:,0], valid_front[:,1], c="g")
# if len(invalid_front) > 0:
# plt.scatter(invalid_front[:,0], invalid_front[:,1], c="r")
# plt.show()
return strategy.parents
def main():
# The cma module uses the numpy random number generator
# numpy.random.seed(128)
MU, LAMBDA = 10, 10
NGEN = 500
verbose = True
create_plot = False
# The MO-CMA-ES algorithm takes a full population as argument
population = [
creator.Individual(x) for x in (numpy.random.uniform(0, 1, (MU, N)))
]
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
strategy = cma.StrategyMultiObjective(population,
sigma=1.0,
mu=MU,
lambda_=LAMBDA)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
fitness_history = []
for gen in range(NGEN):
# Generate a new population
population = toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
fitness_history.append(fit)
# Update the strategy with the evaluated individuals
toolbox.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population), **record)
if verbose:
print((logbook.stream))
if verbose:
print(("Final population hypervolume is %f" %
hypervolume(strategy.parents, [11.0, 11.0])))
# Note that we use a penalty to guide the search to feasible solutions,
# but there is no guarantee that individuals are valid.
# We expect the best individuals will be within bounds or very close.
num_valid = 0
for ind in strategy.parents:
dist = distance(closest_feasible(ind), ind)
if numpy.isclose(dist, 0.0, rtol=1.e-5, atol=1.e-5):
num_valid += 1
print(("Number of valid individuals is %d/%d" %
(num_valid, len(strategy.parents))))
print("Final population:")
print((numpy.asarray(strategy.parents)))
if create_plot:
interactive = 0
if not interactive:
import matplotlib as mpl_tmp
mpl_tmp.use(
'Agg') # Force matplotlib to not use any Xwindows backend.
import matplotlib.pyplot as plt
fig = plt.figure()
plt.title("Multi-objective minimization via MO-CMA-ES")
plt.xlabel("First objective (function) to minimize")
plt.ylabel("Second objective (function) to minimize")
# Limit the scale because our history values include the penalty.
plt.xlim((-0.1, 1.20))
plt.ylim((-0.1, 1.20))
# Plot all history. Note the values include the penalty.
fitness_history = numpy.asarray(fitness_history)
plt.scatter(fitness_history[:, 0],
fitness_history[:, 1],
facecolors='none',
edgecolors="lightblue")
valid_front = numpy.array([
ind.fitness.values for ind in strategy.parents if close_valid(ind)
])
invalid_front = numpy.array([
ind.fitness.values for ind in strategy.parents
if not close_valid(ind)
])
if len(valid_front) > 0:
plt.scatter(valid_front[:, 0], valid_front[:, 1], c="g")
if len(invalid_front) > 0:
plt.scatter(invalid_front[:, 0], invalid_front[:, 1], c="r")
if interactive:
plt.show()
else:
print("Writing cma_mo.png")
plt.savefig("cma_mo.png")
return strategy.parents