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):
n_neighbors = UniformIntegerHyperparameter(name="n_neighbors",
lower=1,
upper=100,
default=1)
weights = CategoricalHyperparameter(name="weights",
choices=["uniform", "distance"],
default="uniform")
metric = UnParametrizedHyperparameter(name="metric", value="minkowski")
algorithm = Constant(name='algorithm', value="auto")
p = CategoricalHyperparameter(name="p", choices=[1, 2, 5], default=2)
leaf_size = Constant(name="leaf_size", value=30)
# Unparametrized
# TODO: If we further parametrize 'metric' we need more metric params
metric = UnParametrizedHyperparameter(name="metric", value="minkowski")
cs = ConfigurationSpace()
cs.add_hyperparameter(n_neighbors)
cs.add_hyperparameter(weights)
cs.add_hyperparameter(metric)
cs.add_hyperparameter(algorithm)
cs.add_hyperparameter(p)
cs.add_hyperparameter(leaf_size)
# Conditions
metric_p = EqualsCondition(parent=metric, child=p, value="minkowski")
cs.add_condition(metric_p)
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):
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):
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):
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):
cs = ConfigurationSpace()
n_iter = cs.add_hyperparameter(
UnParametrizedHyperparameter("n_iter", value=300))
tol = cs.add_hyperparameter(
UniformFloatHyperparameter("tol",
10**-5,
10**-1,
default=10**-4,
log=True))
alpha_1 = cs.add_hyperparameter(
UniformFloatHyperparameter(name="alpha_1",
lower=10**-10,
upper=10**-3,
default=10**-6))
alpha_2 = cs.add_hyperparameter(
UniformFloatHyperparameter(name="alpha_2",
log=True,
lower=10**-10,
upper=10**-3,
default=10**-6))
lambda_1 = cs.add_hyperparameter(
UniformFloatHyperparameter(name="lambda_1",
log=True,
lower=10**-10,
upper=10**-3,
default=10**-6))
lambda_2 = cs.add_hyperparameter(
UniformFloatHyperparameter(name="lambda_2",
log=True,
lower=10**-10,
upper=10**-3,
default=10**-6))
threshold_lambda = cs.add_hyperparameter(
UniformFloatHyperparameter(name="threshold_lambda",
log=True,
lower=10**3,
upper=10**5,
default=10**4))
fit_intercept = cs.add_hyperparameter(
UnParametrizedHyperparameter("fit_intercept", "True"))
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 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 = ConfigurationSpace()
n_estimators = cs.add_hyperparameter(Constant("n_estimators", 100))
criterion = cs.add_hyperparameter(Constant("criterion", "mse"))
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))
# Unparametrized, we use min_samples as regularization
# max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter(
# name="max_leaf_nodes_or_max_depth", value="max_depth")
# CategoricalHyperparameter("max_leaf_nodes_or_max_depth",
# choices=["max_leaf_nodes", "max_depth"], default="max_depth")
# min_weight_fraction_leaf = UniformFloatHyperparameter(
# "min_weight_fraction_leaf", 0.0, 0.1)
# max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes",
# value="None")
bootstrap = cs.add_hyperparameter(
CategoricalHyperparameter("bootstrap", ["True", "False"],
default="False"))
# Conditions
# Not applicable because max_leaf_nodes is no legal value of the parent
#cond_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=max_leaf_nodes,
# parent=max_leaf_nodes_or_max_depth,
# value="max_leaf_nodes")
#cond2_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=use_max_depth,
# parent=max_leaf_nodes_or_max_depth,
# value="max_depth")
#cond_max_depth = EqualsCondition(child=max_depth, parent=use_max_depth,
#value="True")
#cs.add_condition(cond_max_leaf_nodes_or_max_depth)
#cs.add_condition(cond2_max_leaf_nodes_or_max_depth)
#cs.add_condition(cond_max_depth)
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):
cs = ConfigurationSpace()
criterion = cs.add_hyperparameter(Constant('criterion', 'mse'))
splitter = cs.add_hyperparameter(Constant("splitter", "best"))
max_features = cs.add_hyperparameter(Constant('max_features', 1.0))
max_depth = cs.add_hyperparameter(UniformFloatHyperparameter(
'max_depth', 0., 2., default=0.5))
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", 0.0))
max_leaf_nodes = cs.add_hyperparameter(
UnParametrizedHyperparameter("max_leaf_nodes", "None"))
return cs
def get_hyperparameter_search_space(dataset_properties=None):
n_estimators = UniformIntegerHyperparameter(name="n_estimators",
lower=10, upper=100,
default=10)
max_depth = UniformIntegerHyperparameter(name="max_depth",
lower=2, upper=10,
default=5)
min_samples_split = UniformIntegerHyperparameter(name="min_samples_split",
lower=2, upper=20,
default=2)
min_samples_leaf = UniformIntegerHyperparameter(name="min_samples_leaf",
lower=1, upper=20,
default=1)
min_weight_fraction_leaf = Constant('min_weight_fraction_leaf', 1.0)
max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes",
value="None")
cs = ConfigurationSpace()
cs.add_hyperparameter(n_estimators)
cs.add_hyperparameter(max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
cs.add_hyperparameter(min_weight_fraction_leaf)
cs.add_hyperparameter(max_leaf_nodes)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
# Copied from libsvm_c
C = UniformFloatHyperparameter(name="C",
lower=0.03125,
upper=32768,
log=True,
default=1.0)
kernel = CategoricalHyperparameter(
name="kernel",
choices=['linear', 'poly', 'rbf', 'sigmoid'],
default="rbf")
degree = UniformIntegerHyperparameter(name="degree",
lower=1,
upper=5,
default=3)
# Changed the gamma value to 0.0 (is 0.1 for classification)
gamma = UniformFloatHyperparameter(name="gamma",
lower=3.0517578125e-05,
upper=8,
log=True,
default=0.1)
# TODO this is totally ad-hoc
coef0 = UniformFloatHyperparameter(name="coef0",
lower=-1,
upper=1,
default=0)
# probability is no hyperparameter, but an argument to the SVM algo
shrinking = CategoricalHyperparameter(name="shrinking",
choices=["True", "False"],
default="True")
tol = UniformFloatHyperparameter(name="tol",
lower=1e-5,
upper=1e-1,
default=1e-3,
log=True)
max_iter = UnParametrizedHyperparameter("max_iter", -1)
# Random Guess
epsilon = UniformFloatHyperparameter(name="epsilon",
lower=0.001,
upper=1,
default=0.1,
log=True)
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)
cs.add_hyperparameter(epsilon)
degree_depends_on_kernel = InCondition(child=degree,
parent=kernel,
values=('poly', 'rbf',
'sigmoid'))
gamma_depends_on_kernel = InCondition(child=gamma,
parent=kernel,
values=('poly', 'rbf'))
coef0_depends_on_kernel = InCondition(child=coef0,
parent=kernel,
values=('poly', 'sigmoid'))
cs.add_condition(degree_depends_on_kernel)
cs.add_condition(gamma_depends_on_kernel)
cs.add_condition(coef0_depends_on_kernel)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
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)
# Unparametrized
#max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter(
# name="max_leaf_nodes_or_max_depth", value="max_depth")
# CategoricalHyperparameter("max_leaf_nodes_or_max_depth",
# choices=["max_leaf_nodes", "max_depth"], default="max_depth")
max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes",
value="None")
# Copied from 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(
# name="max_features", lower=0.01, upper=0.5, default=0.1)
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(learning_rate)
cs.add_hyperparameter(max_features)
#cs.add_hyperparameter(max_leaf_nodes_or_max_depth)
#cs.add_hyperparameter(max_leaf_nodes)
cs.add_hyperparameter(max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
cs.add_hyperparameter(subsample)
# Conditions
#cond_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=max_leaf_nodes,
# parent=max_leaf_nodes_or_max_depth,
# value="max_leaf_nodes")
#cond2_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=max_depth,
# parent=max_leaf_nodes_or_max_depth,
# value="max_depth")
#cs.add_condition(cond_max_leaf_nodes_or_max_depth)
#cs.add_condition(cond2_max_leaf_nodes_or_max_depth)
return cs
def get_hyperparameter_search_space(dataset_properties=None):
#use_max_depth = CategoricalHyperparameter(
# name="use_max_depth", choices=("True", "False"), default="False")
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default="False")
# Copied from random_forest.py
#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=0.1)
max_features = UniformFloatHyperparameter("max_features",
0.5,
5,
default=1)
min_samples_split = UniformIntegerHyperparameter("min_samples_split",
2,
20,
default=2)
min_samples_leaf = UniformIntegerHyperparameter("min_samples_leaf",
1,
20,
default=1)
# Unparametrized
#max_leaf_nodes_or_max_depth = UnParametrizedHyperparameter(
# name="max_leaf_nodes_or_max_depth", value="max_depth")
# CategoricalHyperparameter("max_leaf_nodes_or_max_depth",
# choices=["max_leaf_nodes", "max_depth"], default="max_depth")
#max_leaf_nodes = UnParametrizedHyperparameter(name="max_leaf_nodes",
# value="None")
# UniformIntegerHyperparameter(
# name="max_leaf_nodes", lower=10, upper=1000, default=)
max_depth = UnParametrizedHyperparameter(name="max_depth",
value="None")
cs = ConfigurationSpace()
cs.add_hyperparameter(n_estimators)
cs.add_hyperparameter(criterion)
cs.add_hyperparameter(max_features)
#cs.add_hyperparameter(use_max_depth)
cs.add_hyperparameter(max_depth)
#cs.add_hyperparameter(max_leaf_nodes_or_max_depth)
cs.add_hyperparameter(min_samples_split)
cs.add_hyperparameter(min_samples_leaf)
#cs.add_hyperparameter(max_leaf_nodes)
cs.add_hyperparameter(bootstrap)
# Conditions
# Not applicable because max_leaf_nodes is no legal value of the parent
#cond_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=max_leaf_nodes,
# parent=max_leaf_nodes_or_max_depth,
# value="max_leaf_nodes")
#cond2_max_leaf_nodes_or_max_depth = \
# EqualsCondition(child=use_max_depth,
# parent=max_leaf_nodes_or_max_depth,
# value="max_depth")
#cond_max_depth = EqualsCondition(child=max_depth, parent=use_max_depth,
#value="True")
#cs.add_condition(cond_max_leaf_nodes_or_max_depth)
#cs.add_condition(cond2_max_leaf_nodes_or_max_depth)
#cs.add_condition(cond_max_depth)
return cs
