def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
penalty = cs.add_hyperparameter(Constant("penalty", "l1"))
loss = cs.add_hyperparameter(
CategoricalHyperparameter("loss", ["hinge", "squared_hinge"],
default="squared_hinge"))
dual = cs.add_hyperparameter(Constant("dual", "False"))
# This is set ad-hoc
tol = cs.add_hyperparameter(
UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default=1e-4,
log=True))
C = cs.add_hyperparameter(
UniformFloatHyperparameter("C",
0.03125,
32768,
log=True,
default=1.0))
multi_class = cs.add_hyperparameter(Constant("multi_class", "ovr"))
# These are set ad-hoc
fit_intercept = cs.add_hyperparameter(Constant("fit_intercept",
"True"))
intercept_scaling = cs.add_hyperparameter(
Constant("intercept_scaling", 1))
penalty_and_loss = ForbiddenAndConjunction(
ForbiddenEqualsClause(penalty, "l1"),
ForbiddenEqualsClause(loss, "hinge"))
cs.add_forbidden_clause(penalty_and_loss)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
loss = CategoricalHyperparameter(
"loss",
["hinge", "log", "modified_huber", "squared_hinge", "perceptron"],
default="hinge")
penalty = CategoricalHyperparameter("penalty",
["l1", "l2", "elasticnet"],
default="l2")
alpha = UniformFloatHyperparameter("alpha",
10**-7,
10**-1,
log=True,
default=0.0001)
l1_ratio = UniformFloatHyperparameter("l1_ratio", 0, 1, default=0.15)
fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True")
n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default=20)
epsilon = UniformFloatHyperparameter("epsilon",
1e-5,
1e-1,
default=1e-4,
log=True)
learning_rate = CategoricalHyperparameter(
"learning_rate", ["optimal", "invscaling", "constant"],
default="optimal")
eta0 = UniformFloatHyperparameter("eta0", 10**-7, 0.1, default=0.01)
power_t = UniformFloatHyperparameter("power_t", 1e-5, 1, default=0.5)
# This does not allow for other resampling methods!
class_weight = CategoricalHyperparameter("class_weight",
["None", "auto"],
default="None")
cs = ConfigurationSpace()
cs.add_hyperparameter(loss)
cs.add_hyperparameter(penalty)
cs.add_hyperparameter(alpha)
cs.add_hyperparameter(l1_ratio)
cs.add_hyperparameter(fit_intercept)
cs.add_hyperparameter(n_iter)
cs.add_hyperparameter(epsilon)
cs.add_hyperparameter(learning_rate)
cs.add_hyperparameter(eta0)
cs.add_hyperparameter(power_t)
cs.add_hyperparameter(class_weight)
# TODO add passive/aggressive here, although not properly documented?
elasticnet = EqualsCondition(l1_ratio, penalty, "elasticnet")
epsilon_condition = EqualsCondition(epsilon, loss, "modified_huber")
# eta0 seems to be always active according to the source code; when
# learning_rate is set to optimial, eta0 is the starting value:
# https://github.com/scikit-learn/scikit-learn/blob/0.15.X/sklearn/linear_model/sgd_fast.pyx
#eta0_and_inv = EqualsCondition(eta0, learning_rate, "invscaling")
#eta0_and_constant = EqualsCondition(eta0, learning_rate, "constant")
#eta0_condition = OrConjunction(eta0_and_inv, eta0_and_constant)
power_t_condition = EqualsCondition(power_t, learning_rate,
"invscaling")
cs.add_condition(elasticnet)
cs.add_condition(epsilon_condition)
cs.add_condition(power_t_condition)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
n_components = UniformIntegerHyperparameter("n_components",
10,
2000,
default=100)
kernel = CategoricalHyperparameter(
'kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf')
degree = UniformIntegerHyperparameter('degree', 2, 5, 3)
gamma = UniformFloatHyperparameter("gamma",
3.0517578125e-05,
8,
log=True,
default=1.0)
coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0)
cs = ConfigurationSpace()
cs.add_hyperparameter(n_components)
cs.add_hyperparameter(kernel)
cs.add_hyperparameter(degree)
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(coef0)
degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
gamma_condition = InCondition(gamma, kernel, ["poly", "rbf"])
cs.add_condition(degree_depends_on_poly)
cs.add_condition(coef0_condition)
cs.add_condition(gamma_condition)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
C = cs.add_hyperparameter(UniformFloatHyperparameter(
"C", 0.03125, 32768, log=True, default=1.0))
loss = cs.add_hyperparameter(CategoricalHyperparameter(
"loss", ["epsilon_insensitive", "squared_epsilon_insensitive"],
default="squared_epsilon_insensitive"))
# Random Guess
epsilon = cs.add_hyperparameter(UniformFloatHyperparameter(
name="epsilon", lower=0.001, upper=1, default=0.1, log=True))
dual = cs.add_hyperparameter(Constant("dual", "False"))
# These are set ad-hoc
tol = cs.add_hyperparameter(UniformFloatHyperparameter(
"tol", 1e-5, 1e-1, default=1e-4, log=True))
fit_intercept = cs.add_hyperparameter(Constant("fit_intercept", "True"))
intercept_scaling = cs.add_hyperparameter(Constant(
"intercept_scaling", 1))
dual_and_loss = ForbiddenAndConjunction(
ForbiddenEqualsClause(dual, "False"),
ForbiddenEqualsClause(loss, "epsilon_insensitive")
)
cs.add_forbidden_clause(dual_and_loss)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
alpha = cs.add_hyperparameter(UniformFloatHyperparameter(
"alpha", 10 ** -5, 10., log=True, default=1.))
fit_intercept = cs.add_hyperparameter(UnParametrizedHyperparameter(
"fit_intercept", "True"))
tol = cs.add_hyperparameter(UniformFloatHyperparameter(
"tol", 1e-5, 1e-1, default=1e-4, log=True))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
nugget = UniformFloatHyperparameter(
name="nugget", lower=0.0001, upper=10, default=0.1, log=True)
thetaL = UniformFloatHyperparameter(
name="thetaL", lower=1e-6, upper=1e-3, default=1e-4, log=True)
thetaU = UniformFloatHyperparameter(
name="thetaU", lower=0.2, upper=10, default=1.0, log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(nugget)
cs.add_hyperparameter(thetaL)
cs.add_hyperparameter(thetaU)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
shrinkage = cs.add_hyperparameter(CategoricalHyperparameter(
"shrinkage", ["None", "auto", "manual"], default="None"))
shrinkage_factor = cs.add_hyperparameter(UniformFloatHyperparameter(
"shrinkage_factor", 0., 1., 0.5))
n_components = cs.add_hyperparameter(UniformIntegerHyperparameter(
'n_components', 1, 250, default=10))
tol = cs.add_hyperparameter(UniformFloatHyperparameter(
"tol", 1e-5, 1e-1, default=1e-4, log=True))
cs.add_condition(EqualsCondition(shrinkage_factor, shrinkage, "manual"))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
loss = cs.add_hyperparameter(
CategoricalHyperparameter("loss",
["ls", "lad", "huber", "quantile"],
default="ls"))
learning_rate = cs.add_hyperparameter(
UniformFloatHyperparameter(name="learning_rate",
lower=0.0001,
upper=1,
default=0.1,
log=True))
n_estimators = cs.add_hyperparameter(Constant("n_estimators", 100))
max_depth = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="max_depth",
lower=1,
upper=10,
default=3))
min_samples_split = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="min_samples_split",
lower=2,
upper=20,
default=2,
log=False))
min_samples_leaf = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="min_samples_leaf",
lower=1,
upper=20,
default=1,
log=False))
min_weight_fraction_leaf = cs.add_hyperparameter(
UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.))
subsample = cs.add_hyperparameter(
UniformFloatHyperparameter(name="subsample",
lower=0.01,
upper=1.0,
default=1.0,
log=False))
max_features = cs.add_hyperparameter(
UniformFloatHyperparameter("max_features", 0.5, 5, default=1))
max_leaf_nodes = cs.add_hyperparameter(
UnParametrizedHyperparameter(name="max_leaf_nodes", value="None"))
alpha = cs.add_hyperparameter(
UniformFloatHyperparameter("alpha",
lower=0.75,
upper=0.99,
default=0.9))
cs.add_condition(InCondition(alpha, loss, ['huber', 'quantile']))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
# base_estimator = Constant(name="base_estimator", value="None")
n_estimators = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="n_estimators",
lower=50,
upper=500,
default=50,
log=False))
learning_rate = cs.add_hyperparameter(
UniformFloatHyperparameter(name="learning_rate",
lower=0.0001,
upper=2,
default=0.1,
log=True))
algorithm = cs.add_hyperparameter(
CategoricalHyperparameter(name="algorithm",
choices=["SAMME.R", "SAMME"],
default="SAMME.R"))
max_depth = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="max_depth",
lower=1,
upper=10,
default=1,
log=False))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
#n_estimators = UniformIntegerHyperparameter(
# "n_estimators", 10, 100, default=10)
n_estimators = Constant("n_estimators", 100)
criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"],
default="gini")
#max_features = UniformFloatHyperparameter(
# "max_features", 0.01, 0.5, default=0.2)
max_features = UniformFloatHyperparameter("max_features",
0.5,
5,
default=1)
max_depth = UnParametrizedHyperparameter("max_depth", "None")
min_samples_split = UniformIntegerHyperparameter("min_samples_split",
2,
20,
default=2)
min_samples_leaf = UniformIntegerHyperparameter("min_samples_leaf",
1,
20,
default=1)
max_leaf_nodes = UnParametrizedHyperparameter("max_leaf_nodes", "None")
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default="True")
cs = ConfigurationSpace()
cs.add_hyperparameter(n_estimators)
cs.add_hyperparameter(criterion)
cs.add_hyperparameter(max_features)
cs.add_hyperparameter(max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
cs.add_hyperparameter(max_leaf_nodes)
cs.add_hyperparameter(bootstrap)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
criterion = Constant(name="criterion", value="mse")
# Copied from classification/random_forest.py
#n_estimators = UniformIntegerHyperparameter(
# name="n_estimators", lower=10, upper=100, default=10, log=False)
n_estimators = Constant("n_estimators", 100)
max_features = UniformFloatHyperparameter(
"max_features", 0.5, 5, default=1)
max_depth = UnParametrizedHyperparameter("max_depth", "None")
min_samples_split = UniformIntegerHyperparameter(
name="min_samples_split", lower=2, upper=20, default=2, log=False)
min_samples_leaf = UniformIntegerHyperparameter(
name="min_samples_leaf", lower=1, upper=20, default=1, log=False)
bootstrap = CategoricalHyperparameter(
name="bootstrap", choices=["True", "False"], default="True")
cs = ConfigurationSpace()
cs.add_hyperparameter(n_estimators)
cs.add_hyperparameter(max_features)
cs.add_hyperparameter(max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
cs.add_hyperparameter(bootstrap)
cs.add_hyperparameter(criterion)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
n_estimators = cs.add_hyperparameter(Constant("n_estimators", 100))
criterion = cs.add_hyperparameter(
CategoricalHyperparameter("criterion", ["gini", "entropy"],
default="gini"))
max_features = cs.add_hyperparameter(
UniformFloatHyperparameter("max_features", 0.5, 5, default=1))
max_depth = cs.add_hyperparameter(
UnParametrizedHyperparameter(name="max_depth", value="None"))
min_samples_split = cs.add_hyperparameter(
UniformIntegerHyperparameter("min_samples_split", 2, 20,
default=2))
min_samples_leaf = cs.add_hyperparameter(
UniformIntegerHyperparameter("min_samples_leaf", 1, 20, default=1))
min_weight_fraction_leaf = cs.add_hyperparameter(
Constant('min_weight_fraction_leaf', 0.))
bootstrap = cs.add_hyperparameter(
CategoricalHyperparameter("bootstrap", ["True", "False"],
default="False"))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
N = UniformIntegerHyperparameter("N", 5, 20, default=10)
precond = UniformFloatHyperparameter("precond", 0, 0.5, default=0.1)
cs = ConfigurationSpace()
cs.add_hyperparameter(N)
cs.add_hyperparameter(precond)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
reg_param = UniformFloatHyperparameter('reg_param',
0.0,
10.0,
default=0.5)
cs = ConfigurationSpace()
cs.add_hyperparameter(reg_param)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConstrainedFeedNet.get_hyperparameter_search_space()
solver = Constant(name='solver', value='adam')
cs.add_hyperparameter(solver)
beta1 = UniformFloatHyperparameter("beta1",
1e-4,
0.1,
log=True,
default=0.1)
beta2 = UniformFloatHyperparameter("beta2",
1e-4,
0.1,
log=True,
default=0.1)
cs.add_hyperparameter(beta1)
cs.add_hyperparameter(beta2)
return cs
def _sample_UniformIntegerHyperparameter(self, uihp):
ufhp = UniformFloatHyperparameter(uihp.name,
uihp.lower - 0.4999,
uihp.upper + 0.4999,
log=uihp.log, q=uihp.q,
default=uihp.default)
ihp = self._sample_UniformFloatHyperparameter(ufhp)
return uihp.instantiate(int(np.round(ihp.value, 0)))
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConstrainedFeedNet.get_hyperparameter_search_space()
solver = Constant(name='solver', value='adadelta')
cs.add_hyperparameter(solver)
rho = UniformFloatHyperparameter('rho', 0.0, 1.0, default=0.95)
cs.add_hyperparameter(rho)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
loss = cs.add_hyperparameter(CategoricalHyperparameter("loss",
["squared_loss", "huber", "epsilon_insensitive", "squared_epsilon_insensitive"],
default="squared_loss"))
penalty = cs.add_hyperparameter(CategoricalHyperparameter(
"penalty", ["l1", "l2", "elasticnet"], default="l2"))
alpha = cs.add_hyperparameter(UniformFloatHyperparameter(
"alpha", 10e-7, 1e-1, log=True, default=0.01))
l1_ratio = cs.add_hyperparameter(UniformFloatHyperparameter(
"l1_ratio", 1e-9, 1., log=True, default=0.15))
fit_intercept = cs.add_hyperparameter(UnParametrizedHyperparameter(
"fit_intercept", "True"))
n_iter = cs.add_hyperparameter(UniformIntegerHyperparameter(
"n_iter", 5, 1000, log=True, default=20))
epsilon = cs.add_hyperparameter(UniformFloatHyperparameter(
"epsilon", 1e-5, 1e-1, default=1e-4, log=True))
learning_rate = cs.add_hyperparameter(CategoricalHyperparameter(
"learning_rate", ["optimal", "invscaling", "constant"],
default="optimal"))
eta0 = cs.add_hyperparameter(UniformFloatHyperparameter(
"eta0", 10 ** -7, 0.1, default=0.01))
power_t = cs.add_hyperparameter(UniformFloatHyperparameter(
"power_t", 1e-5, 1, default=0.5))
average = cs.add_hyperparameter(CategoricalHyperparameter(
"average", ["False", "True"], default="False"))
# TODO add passive/aggressive here, although not properly documented?
elasticnet = EqualsCondition(l1_ratio, penalty, "elasticnet")
epsilon_condition = InCondition(epsilon, loss,
["huber", "epsilon_insensitive", "squared_epsilon_insensitive"])
# eta0 seems to be always active according to the source code; when
# learning_rate is set to optimial, eta0 is the starting value:
# https://github.com/scikit-learn/scikit-learn/blob/0.15.X/sklearn/linear_model/sgd_fast.pyx
# eta0_and_inv = EqualsCondition(eta0, learning_rate, "invscaling")
#eta0_and_constant = EqualsCondition(eta0, learning_rate, "constant")
#eta0_condition = OrConjunction(eta0_and_inv, eta0_and_constant)
power_t_condition = EqualsCondition(power_t, learning_rate,
"invscaling")
cs.add_condition(elasticnet)
cs.add_condition(epsilon_condition)
cs.add_condition(power_t_condition)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
C = UniformFloatHyperparameter("C",
0.03125,
32768,
log=True,
default=1.0)
# No linear kernel here, because we have liblinear
kernel = CategoricalHyperparameter(name="kernel",
choices=["rbf", "poly", "sigmoid"],
default="rbf")
degree = UniformIntegerHyperparameter("degree", 1, 5, default=3)
gamma = UniformFloatHyperparameter("gamma",
3.0517578125e-05,
8,
log=True,
default=0.1)
# TODO this is totally ad-hoc
coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0)
# probability is no hyperparameter, but an argument to the SVM algo
shrinking = CategoricalHyperparameter("shrinking", ["True", "False"],
default="True")
tol = UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default=1e-4,
log=True)
# cache size is not a hyperparameter, but an argument to the program!
max_iter = UnParametrizedHyperparameter("max_iter", -1)
cs = ConfigurationSpace()
cs.add_hyperparameter(C)
cs.add_hyperparameter(kernel)
cs.add_hyperparameter(degree)
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(coef0)
cs.add_hyperparameter(shrinking)
cs.add_hyperparameter(tol)
cs.add_hyperparameter(max_iter)
degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
cs.add_condition(degree_depends_on_poly)
cs.add_condition(coef0_condition)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
keep_variance = UniformFloatHyperparameter(
"keep_variance", 0.5, 0.9999, default=0.9999)
whiten = CategoricalHyperparameter(
"whiten", ["False", "True"], default="False")
cs = ConfigurationSpace()
cs.add_hyperparameter(keep_variance)
cs.add_hyperparameter(whiten)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
gamma = UniformFloatHyperparameter(
"gamma", 0.3, 2., default=1.0)
n_components = UniformIntegerHyperparameter(
"n_components", 50, 10000, default=100, log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(n_components)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
if dataset_properties is not None and \
(dataset_properties.get("sparse") is True or
dataset_properties.get("signed") is False):
allow_chi2 = False
else:
allow_chi2 = True
possible_kernels = ['poly', 'rbf', 'sigmoid', 'cosine']
if allow_chi2:
possible_kernels.append("chi2")
kernel = CategoricalHyperparameter('kernel', possible_kernels, 'rbf')
degree = UniformIntegerHyperparameter('degree', 2, 5, 3)
gamma = UniformFloatHyperparameter("gamma",
3.0517578125e-05,
8,
log=True,
default=0.1)
coef0 = UniformFloatHyperparameter("coef0", -1, 1, default=0)
n_components = UniformIntegerHyperparameter("n_components",
50,
10000,
default=100,
log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(kernel)
cs.add_hyperparameter(degree)
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(coef0)
cs.add_hyperparameter(n_components)
degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
gamma_kernels = ["poly", "rbf", "sigmoid"]
if allow_chi2:
gamma_kernels.append("chi2")
gamma_condition = InCondition(gamma, kernel, gamma_kernels)
cs.add_condition(degree_depends_on_poly)
cs.add_condition(coef0_condition)
cs.add_condition(gamma_condition)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
alpha = UniformFloatHyperparameter(name="alpha",
lower=0.0001,
upper=10,
default=1.0,
log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(alpha)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
loss = CategoricalHyperparameter(
name="loss", choices=["ls", "lad"],
default='ls') #, "huber", "quantile"], default='ls')
learning_rate = UniformFloatHyperparameter(name="learning_rate",
lower=0.0001,
upper=1,
default=0.1,
log=True)
subsample = UniformFloatHyperparameter(name="subsample",
lower=0.01,
upper=1.0,
default=1.0,
log=False)
n_estimators = Constant("n_estimators", 100)
max_features = UniformFloatHyperparameter("max_features",
0.5,
5,
default=1)
max_depth = UniformIntegerHyperparameter(name="max_depth",
lower=1,
upper=10,
default=3)
min_samples_split = UniformIntegerHyperparameter(
name="min_samples_split", lower=2, upper=20, default=2, log=False)
min_samples_leaf = UniformIntegerHyperparameter(
name="min_samples_leaf", lower=1, upper=20, default=1, log=False)
cs = ConfigurationSpace()
cs.add_hyperparameter(n_estimators)
cs.add_hyperparameter(loss)
cs.add_hyperparameter(learning_rate)
cs.add_hyperparameter(max_features)
cs.add_hyperparameter(max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
cs.add_hyperparameter(subsample)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
percentile = UniformFloatHyperparameter(
"percentile", lower=1, upper=99, default=50)
score_func = UnParametrizedHyperparameter(
name="score_func", value="f_regression")
cs = ConfigurationSpace()
cs.add_hyperparameter(percentile)
cs.add_hyperparameter(score_func)
return cs
def _lr_policy_configuration_space(cs, policy=None):
if policy == 'inv':
lr_policy = Constant(name='lr_policy', value='inv')
gamma = UniformFloatHyperparameter(name="gamma",
lower=1e-2,
upper=1.0,
log=True,
default=1e-2)
power = UniformFloatHyperparameter("power", 0.0, 1.0, default=0.5)
cs.add_hyperparameter(lr_policy)
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(power)
elif policy == 'exp':
lr_policy = Constant(name='lr_policy', value='exp')
gamma = UniformFloatHyperparameter(name="gamma",
lower=0.7,
upper=1.0,
default=0.79)
cs.add_hyperparameter(lr_policy)
cs.add_hyperparameter(gamma)
elif policy == 'step':
lr_policy = Constant(name='lr_policy', value='step')
gamma = UniformFloatHyperparameter(name="gamma",
lower=1e-2,
upper=1.0,
log=True,
default=1e-2)
epoch_step = CategoricalHyperparameter("epoch_step", [6, 8, 12],
default=8)
cs.add_hyperparameter(lr_policy)
cs.add_hyperparameter(gamma)
cs.add_hyperparameter(epoch_step)
else:
lr_policy = Constant(name='lr_policy', value='fixed')
cs.add_hyperparameter(lr_policy)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
loss = cs.add_hyperparameter(Constant("loss", "deviance"))
learning_rate = cs.add_hyperparameter(
UniformFloatHyperparameter(name="learning_rate",
lower=0.0001,
upper=1,
default=0.1,
log=True))
n_estimators = cs.add_hyperparameter(Constant("n_estimators", 100))
max_depth = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="max_depth",
lower=1,
upper=10,
default=3))
min_samples_split = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="min_samples_split",
lower=2,
upper=20,
default=2,
log=False))
min_samples_leaf = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="min_samples_leaf",
lower=1,
upper=20,
default=1,
log=False))
min_weight_fraction_leaf = cs.add_hyperparameter(
UnParametrizedHyperparameter("min_weight_fraction_leaf", 0.))
subsample = cs.add_hyperparameter(
UniformFloatHyperparameter(name="subsample",
lower=0.01,
upper=1.0,
default=1.0,
log=False))
max_features = cs.add_hyperparameter(
UniformFloatHyperparameter("max_features", 0.5, 5, default=1))
max_leaf_nodes = cs.add_hyperparameter(
UnParametrizedHyperparameter(name="max_leaf_nodes", value="None"))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
loss = CategoricalHyperparameter("loss", ["hinge", "squared_hinge"],
default="hinge")
fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True")
n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, default=20)
C = UniformFloatHyperparameter("C", 1e-5, 10, 1, log=True)
cs = ConfigurationSpace()
cs.add_hyperparameter(loss)
cs.add_hyperparameter(fit_intercept)
cs.add_hyperparameter(n_iter)
cs.add_hyperparameter(C)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConstrainedFeedNet.get_hyperparameter_search_space()
solver = Constant(name='solver', value='momentum')
cs.add_hyperparameter(solver)
momentum = UniformFloatHyperparameter("momentum",
0.3,
0.999,
default=0.9)
cs.add_hyperparameter(momentum)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
# Constrain the search space of the DeepFeedNet
# Fixed Architecture for MNIST
batch_size = Constant(name='batch_size', value=963)
# TODO: Decimal library to work around floating point issue
dropout_layer_1 = Constant(name='dropout_layer_1', value=0.39426633933)
dropout_output = Constant(name='dropout_output', value=0.085813712701)
num_layers = Constant(name='num_layers', value='c')
num_units_layer_1 = Constant(name='num_units_layer_1', value=1861)
number_updates = Constant(name='number_updates', value=1105)
std_layer_1 = Constant(name='std_layer_1', value=0.00351015701)
# To Optimize for all cases
l2 = UniformFloatHyperparameter("lambda2",
1e-6,
1e-2,
log=True,
default=1e-3)
lr = UniformFloatHyperparameter("learning_rate",
1e-4,
1e-1,
log=True,
default=1e-2)
cs = ConfigurationSpace()
cs.add_hyperparameter(batch_size)
cs.add_hyperparameter(number_updates)
cs.add_hyperparameter(num_layers)
cs.add_hyperparameter(num_units_layer_1)
cs.add_hyperparameter(dropout_layer_1)
cs.add_hyperparameter(dropout_output)
cs.add_hyperparameter(std_layer_1)
cs.add_hyperparameter(lr)
cs.add_hyperparameter(l2)
return cs
