def testing(trainData, testData, methodIds):
def evaluateFunc(config):
return transform(config, trainData, testData)
toolbox = base.Toolbox()
creator.create(
"FitnessMin",
base.Fitness,
weights=[-1.0],
)
creator.create("Individual",
array.array,
typecode='d',
fitness=creator.FitnessMin)
toolbox.register("evaluate", evaluateFunc)
pop = [creator.Individual(methodIds)]
hof = tools.HallOfFame(1)
fitness = toolbox.evaluate(pop[0])
pop[0].fitness.values = fitness
hof.update(pop)
best = hof[0]
y_predict, y_acutal = abe_execute(S=get_setting_obj(best),
train=trainData,
test=testData)
return mre_calc(y_predict, y_acutal), sa_calc(y_predict, y_acutal), best
def random_strategy(randomTimes, trainData, testData):
"""
:param randomTimes:
:param trainData:
:param testData:
:return: mre, sa, best configuration
"""
def evaluateFunc(config):
return transform(config, trainData, testData)
toolbox = base.Toolbox()
creator.create(
"FitnessMin",
base.Fitness,
weights=[-1.0],
)
creator.create("Individual",
array.array,
typecode='d',
fitness=creator.FitnessMin)
toolbox.register("evaluate", evaluateFunc)
pop = [creator.Individual(randlist()) for _ in range(randomTimes)]
hof = tools.HallOfFame(1)
for ind in pop:
fitness = toolbox.evaluate(ind)
ind.fitness.values = fitness
hof.update(pop)
best = hof[0]
y_predict, y_acutal = abe_execute(S=get_setting_obj(best),
train=trainData,
test=testData)
return mre_calc(y_predict, y_acutal), sa_calc(y_predict, y_acutal), best
def de_estimate(NGEN, trainData, testData):
"""
:param NGEN: list or int
:param trainData:
:param testData:
:return: mre, sa, best configuration. IF NGEN IS A LIST, RETURN A LIST [[mre,sa, best confi][mre,sa,best config],...]
"""
def evaluateFunc(config):
return transform(config, trainData, testData)
# Differential evolution parameters
CR = 0.5
F = 1
MU = 20
toolbox = base.Toolbox()
creator.create(
"FitnessMin",
base.Fitness,
weights=[-1.0],
)
creator.create("Individual",
array.array,
typecode='d',
fitness=creator.FitnessMin)
toolbox.register("select", tools.selRandom, k=3)
toolbox.register("evaluate", evaluateFunc)
pop = [creator.Individual(randlist()) for _ in range(MU)]
hof = tools.HallOfFame(1)
for ind in pop:
fitness = toolbox.evaluate(ind)
ind.fitness.values = fitness
if type(NGEN) is not list:
NGEN = [NGEN]
bests = list()
for g in range(1, max(NGEN) + 1):
for k, agent in enumerate(pop):
a, b, c = toolbox.select(pop)
y = toolbox.clone(agent)
index = random.randrange(NDIM)
lis = [1, 7, 2, 5, 3, 5]
for i, value in enumerate(agent):
if i == index or random.random() < CR:
y[i] = (a[i] + F * (b[i] - c[i])) % lis[i]
y.fitness.values = toolbox.evaluate(y)
if y.fitness > agent.fitness:
pop[k] = y
hof.update(pop)
if g in NGEN:
bests.append(hof[0])
RES = list()
for best in bests:
y_predict, y_acutal = abe_execute(S=get_setting_obj(best),
train=trainData,
test=testData)
RES.append([
mre_calc(y_predict, y_acutal),
sa_calc(y_predict, y_acutal), best
])
if len(RES) == 1:
return RES[0]
else:
return RES