def get_space():
space = HyperSpace()
with space.as_default():
id1 = Identity(p1=Choice(['a', 'b']),
p2=Int(1, 100),
p3=Real(0, 1.0))
return space
def nn(self):
solver = Choice(['lbfgs', 'sgd', 'adam'])
return dict(
cls=MLPClassifier,
max_iter=Int(500, 5000, step=500),
activation=Choice(['identity', 'logistic', 'tanh', 'relu']),
solver=solver,
learning_rate=Choice(['constant', 'invscaling', 'adaptive']),
learning_rate_init_stub=Cascade(partial(self._cascade, self._nn_learning_rate_init, 'slvr'), slvr=solver),
random_state=randint(),
)
def get_space():
space = HyperSpace()
with space.as_default():
p1 = Int(1, 100)
p2 = Choice(['a', 'b', 'c'])
p3 = Bool()
p4 = Real(0.0, 1.0)
id1 = Identity(p1=p1)
id2 = Identity(p2=p2)(id1)
id3 = Identity(p3=p3)(id2)
id4 = Identity(p4=p4)(id3)
return space
def get_space():
space = HyperSpace()
with space.as_default():
id1 = Identity(p1=Int(0, 10), p2=Choice(['a', 'b']))
id2 = Identity(p3=Real(0., 1.), p4=Bool())(id1)
return space
def func_early_stopping(p1=Choice(['a', 'b'], random_state=np.random.RandomState(9527)),
p2=Int(1, 10, 2, random_state=np.random.RandomState(9527)),
p3=Real(1.0, 5.0, random_state=np.random.RandomState(9527)),
p4=9):
print(f'p1:{p1},p2:{p2},p3{p3},p4:{p4}')
return 0.6