def svm_from_cfg(cfg):
""" Creates a SVM based on a configuration and evaluates it on the
iris-dataset using cross-validation.
Parameters:
-----------
cfg: Configuration (ConfigSpace.ConfigurationSpace.Configuration)
Configuration containing the parameters.
Configurations are indexable!
Returns:
--------
A crossvalidated mean score for the svm on the loaded data-set.
"""
# For deactivated parameters, the configuration stores None-values.
# This is not accepted by the SVM, so we remove them.
# cfg = {k : cfg[k] for k in cfg if cfg[k]}
# We translate boolean values:
# cfg["shrinking"] = True if cfg["shrinking"] == "true" else False
# And for gamma, we set it to a fixed value or to "auto" (if used)
# if "gamma" in cfg:
# cfg["gamma"] = cfg["gamma_value"] if cfg["gamma"] == "value" else "auto"
# cfg.pop("gamma_value", None) # Remove "gamma_value"
np.random.seed(1)
x = np.array(cfg.values())
# regr = svm.SVR(degree=2, max_iter=1e5, **cfg)
# scores = cross_val_score(regr, X, y, cv=5, scoring='r2')
return benchmarks.himmelblau(x)[0]
def check_stop(self):
_, f, __ = self.best.get()
if f <= self.ftarget:
self.stop_dict["ftarget"] = f
if self.countevals >= self.max_FEs:
self.stop_dict["FEs"] = self.countevals
return bool(self.stop_dict)
dim = 2
n_point = 8
max_FEs = 30 * n_point
obj_fun = lambda x: benchmarks.himmelblau(x)[0]
lb, ub = -6, 6
search_space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=0) # Ordinary Kriging
# autocorrelation parameters of GPR
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
def main():
np.random.seed(64)
pop = toolbox.population(n=150)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
#pop,hof = algorithms.eaSimple(pop,toolbox,cxpb=0.5,mutpb=0.01,ngen=200,stats=stats,halloffame=hof,verbose=True)
fitness = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitness):
ind.fitness.values = fit
min_fit = 0
nvmap = noveltymap(5, POPNUM)
step = 0
#print("gen ","min ""max ","mean")
novflag = 0
for i in range(NGEN):
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = list(map(toolbox.clone, offspring))
# Apply crossover and mutation on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
# cross two individuals with probability CXPB
if random.random() < CXPB:
toolbox.mate(child1, child2)
# fitness values of the children
# must be recalculated later
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
# mutate an individual with probability MUTPB
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
if novflag == 2:
#print("nov")
novflag = 0
_pop = nvmap.popInd(pop)
for off, _ind in zip(offspring, _pop):
off = _ind.xy
off.fitness.values = _ind.novelty,
pop[:] = offspring
fits = [benchmarks.himmelblau(ind)[0] for ind in pop]
else:
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
"""
for ind in invalid_ind:
nvmap.ax.scatter(ind[0],ind[1],c='blue',marker='.')
"""
fitness = list(map(toolbox.evaluate, invalid_ind))
for ind, fit in zip(invalid_ind, fitness):
ind.fitness.values = fit
# The population is entirely replaced by the offspring
pop[:] = offspring
# Gather all the fitnesses in one list and print the stats
fits = [ind.fitness.values[0] for ind in pop]
hof.update(pop)
length = len(pop)
mean = sum(fits) / length
#sum2 = sum(x*x for x in fits)
#std = abs(sum2 / length - mean**2)**0.5
if abs(min(fits) - min_fit) == 0.001:
novflag += 1
else:
novflag = 0
#print(i ,min(fits) ,max(fits) ,mean)
min_fit = min(fits)
if min(fits) <= np.exp(-10):
step += 1
break
#print("gen:",i," Min %s" % min(fits)," Max %s" % max(fits)," Avg %s" % mean)
#print("gen:",i," Min %s" % min(fits)," Max %s" % max(fits)," Avg %s" % mean," Std %s" % std)
#print(i,max(fits),mean)
#nvmap.fig.show()
#time.sleep(100000000)
return pop, hof, step
def untuple(sol):
return benchmarks.himmelblau(sol)[0]
def calFit(self, ind):
return benchmarks.himmelblau(ind)[0]
def himmelblau_arg0(sol):
return np.nan if w_obstacles and sol[2] == 1 else benchmarks.himmelblau(sol[:2])[0]
def evalBenchmark(individual):
return benchmarks.himmelblau(individual)
def himmelblau_arg0(sol):
return benchmarks.himmelblau(sol)[0]
def evaluate(self, x):
return himmelblau(x)[0]
def himmelblau_arg0(sol):
return benchmarks.himmelblau(sol)[0]
