from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
n_estimators = UniformIntegerHyperparameter("n_estimators", 50, 500, default_value=100)
criterion = CategoricalHyperparameter(
"criterion", ["gini", "entropy"], default_value="gini")
max_features = UniformFloatHyperparameter(
"max_features", 0., 1., default_value=0.5)
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter(
"min_samples_split", 0., 1., default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter(
"min_samples_leaf", 0., 0.5, default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
bootstrap = CategoricalHyperparameter(
"bootstrap", ["True", "False"], default_value="True")
cs.add_hyperparameters([n_estimators, criterion, max_features, max_depth, min_samples_split, min_samples_leaf,
min_weight_fraction_leaf, max_leaf_nodes,
bootstrap, min_impurity_decrease])
json_utils.write_cs_to_json_file(cs, "ExtraTreesRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
UniformIntegerHyperparameter
from ConfigSpace.forbidden import ForbiddenInClause, \
ForbiddenAndConjunction, ForbiddenEqualsClause
from automl.utl import json_utils
cs = ConfigurationSpace()
n_clusters = UniformIntegerHyperparameter("n_clusters", 2, 400, 2)
affinity = CategoricalHyperparameter(
"affinity", ["euclidean", "manhattan", "cosine", "l1", "l2"], "euclidean")
linkage = CategoricalHyperparameter("linkage",
["ward", "complete", "average", "single"],
"ward")
cs.add_hyperparameters([n_clusters, affinity, linkage])
affinity_and_linkage = ForbiddenAndConjunction(
ForbiddenAndConjunction(ForbiddenEqualsClause(affinity, "manhattan"),
ForbiddenEqualsClause(affinity, "cosine"),
ForbiddenEqualsClause(affinity, "l1"),
ForbiddenEqualsClause(affinity, "l2")),
ForbiddenEqualsClause(linkage, "ward"))
cs.add_forbidden_clause(affinity_and_linkage)
json_utils.write_cs_to_json_file(cs, "FeatureAgglomeration")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter
from automl.utl import json_utils
gamma = UniformFloatHyperparameter("gamma",
3.0517578125e-05,
8,
default_value=1.0,
log=True)
n_components = UniformIntegerHyperparameter("n_components",
50,
10000,
default_value=100,
log=True)
cs = ConfigurationSpace()
cs.add_hyperparameters([gamma, n_components])
json_utils.write_cs_to_json_file(cs, "RBFSampler")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, \
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
alpha = UniformFloatHyperparameter(name="alpha",
lower=1e-14,
upper=1.0,
default_value=1e-10,
log=True)
cs.add_hyperparameter(alpha)
json_utils.write_cs_to_json_file(cs, "GaussianProcessRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
var_smoothing = UniformFloatHyperparameter("var_smoothing",
1e-11,
1e-7,
default_value=1e-9,
log=True)
cs.add_hyperparameter(var_smoothing)
json_utils.write_cs_to_json_file(cs, "GaussianNB")
default_value=100)
criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"],
default_value="gini")
max_features = UniformFloatHyperparameter("max_features",
0.,
1.,
default_value=0.5)
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter("min_samples_split",
0.,
1.,
default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default_value="True")
cs.add_hyperparameters([
n_estimators, criterion, max_features, max_depth, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap,
min_impurity_decrease
])
json_utils.write_cs_to_json_file(cs, "ExtraTreesClassifier")
0.,
1.,
default_value=0.5)
max_depth = Constant("max_depth_none", "None")
min_samples_split = UniformFloatHyperparameter("min_samples_split",
0.,
1.,
default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default_value="True")
subsample = UniformFloatHyperparameter(name="subsample",
lower=0.01,
upper=1.0,
default_value=1.0)
cs.add_hyperparameters([
loss, learning_rate, n_estimators, max_depth, criterion, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, subsample, max_features,
max_leaf_nodes, min_impurity_decrease
])
json_utils.write_cs_to_json_file(cs, "GradientBoostingClassifier")
upper=10**-3,
default_value=10**-6)
alpha_2 = UniformFloatHyperparameter(name="alpha_2",
log=True,
lower=10**-10,
upper=10**-3,
default_value=10**-6)
lambda_1 = UniformFloatHyperparameter(name="lambda_1",
log=True,
lower=10**-10,
upper=10**-3,
default_value=10**-6)
lambda_2 = UniformFloatHyperparameter(name="lambda_2",
log=True,
lower=10**-10,
upper=10**-3,
default_value=10**-6)
threshold_lambda = UniformFloatHyperparameter(name="threshold_lambda",
log=True,
lower=10**3,
upper=10**5,
default_value=10**4)
fit_intercept = Constant("fit_intercept", "True")
cs.add_hyperparameters([
n_iter, tol, alpha_1, alpha_2, lambda_1, lambda_2, threshold_lambda,
fit_intercept
])
json_utils.write_cs_to_json_file(cs, "ARDRegression")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
norm = CategoricalHyperparameter("norm", ["l1", "l2", "max"], "l2")
cs.add_hyperparameter(norm)
json_utils.write_cs_to_json_file(cs, "Normalizer")
0.03125,
32768,
log=True,
default_value=1.0)
loss = CategoricalHyperparameter(
"loss", ["epsilon_insensitive", "squared_epsilon_insensitive"],
default_value="squared_epsilon_insensitive")
epsilon = UniformFloatHyperparameter(name="epsilon",
lower=0.001,
upper=1,
default_value=0.1,
log=True)
dual = Constant("dual", "False")
tol = UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default_value=1e-4,
log=True)
fit_intercept = Constant("fit_intercept", "True")
intercept_scaling = Constant("intercept_scaling", 1)
cs.add_hyperparameters(
[C, loss, epsilon, dual, tol, fit_intercept, intercept_scaling])
dual_and_loss = ForbiddenAndConjunction(
ForbiddenEqualsClause(dual, "False"),
ForbiddenEqualsClause(loss, "epsilon_insensitive"))
cs.add_forbidden_clause(dual_and_loss)
json_utils.write_cs_to_json_file(cs, "LinearSVR")
default_value=100)
criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"],
default_value="gini")
max_features = UniformFloatHyperparameter("max_features",
0.,
1.,
default_value=1.)
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter("min_samples_split",
0.,
1.,
default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default_value="True")
cs.add_hyperparameters([
n_estimators, criterion, max_features, max_depth, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap,
min_impurity_decrease
])
json_utils.write_cs_to_json_file(cs, "RandomForestClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
CategoricalHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
n_quantiles = UniformIntegerHyperparameter('n_quantiles',
lower=10,
upper=2000,
default_value=1000)
output_distribution = CategoricalHyperparameter('output_distribution',
['uniform', 'normal'])
cs.add_hyperparameters((n_quantiles, output_distribution))
json_utils.write_cs_to_json_file(cs, "QuantileTransformer")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
UniformIntegerHyperparameter, Constant
from ConfigSpace.conditions import EqualsCondition
from automl.utl import json_utils
cs = ConfigurationSpace()
n_components = Constant(
"n_components", "None")
algorithm = CategoricalHyperparameter('algorithm',
['parallel', 'deflation'], 'parallel')
whiten = CategoricalHyperparameter('whiten',
['False', 'True'], 'False')
fun = CategoricalHyperparameter(
'fun', ['logcosh', 'exp', 'cube'], 'logcosh')
cs.add_hyperparameters([n_components, algorithm, whiten, fun])
cs.add_condition(EqualsCondition(n_components, whiten, "True"))
json_utils.write_cs_to_json_file(cs, "FastICA")
default_value=100)
criterion = CategoricalHyperparameter("criterion",
['mse', 'friedman_mse', 'mae'])
max_features = UniformFloatHyperparameter("max_features",
0.,
1.,
default_value=0.5)
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter("min_samples_split",
0.,
1.,
default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
bootstrap = CategoricalHyperparameter("bootstrap", ["True", "False"],
default_value="True")
cs.add_hyperparameters([
n_estimators, criterion, max_features, max_depth, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap,
min_impurity_decrease
])
json_utils.write_cs_to_json_file(cs, "RandomForestRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter
from ConfigSpace.conditions import InCondition, EqualsCondition
from automl.utl import json_utils
cs = ConfigurationSpace()
kernel = CategoricalHyperparameter('kernel', ['poly', 'rbf', 'sigmoid', 'cosine'], 'rbf')
n_components = UniformIntegerHyperparameter(
"n_components", 50, 10000, default_value=100, log=True)
gamma = UniformFloatHyperparameter("gamma", 3.0517578125e-05, 8,
log=True, default_value=0.1)
degree = UniformIntegerHyperparameter('degree', 2, 5, 3)
coef0 = UniformFloatHyperparameter("coef0", -1, 1, default_value=0)
cs.add_hyperparameters([kernel, degree, gamma, coef0, n_components])
degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
gamma_kernels = ["poly", "rbf", "sigmoid"]
gamma_condition = InCondition(gamma, kernel, gamma_kernels)
cs.add_conditions([degree_depends_on_poly, coef0_condition, gamma_condition])
json_utils.write_cs_to_json_file(cs, "Nystroem")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, \
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
# the smoothing parameter is a non-negative float
# I will limit it to 100 and put it on a logarithmic scale. (SF)
# Please adjust that, if you know a proper range, this is just a guess.
alpha = UniformFloatHyperparameter(name="alpha", lower=1e-2, upper=100,
default_value=1, log=True)
fit_prior = CategoricalHyperparameter(name="fit_prior",
choices=["True", "False"],
default_value="True")
cs.add_hyperparameters([alpha, fit_prior])
json_utils.write_cs_to_json_file(cs, "MultinomialNB")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
n_estimators = UniformIntegerHyperparameter(name="n_estimators",
lower=50,
upper=500,
default_value=100)
learning_rate = UniformFloatHyperparameter(name="learning_rate",
lower=0.,
upper=1.,
default_value=1.)
algorithm = CategoricalHyperparameter(name="algorithm",
choices=["SAMME.R", "SAMME"],
default_value="SAMME.R")
cs.add_hyperparameters([n_estimators, learning_rate, algorithm])
json_utils.write_cs_to_json_file(cs, "AdaBoostClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
shrinkage = UniformFloatHyperparameter("shrinkage", 0., 1., 0.5)
tol = UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default_value=1e-4,
log=True)
cs.add_hyperparameters([shrinkage, tol])
json_utils.write_cs_to_json_file(cs, "LinearDiscriminantAnalysis")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
CategoricalHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
n_components = UniformFloatHyperparameter(
"n_components", 0.5, 0.9999, default_value=0.9999)
whiten = CategoricalHyperparameter(
"whiten", ["False", "True"], default_value="False")
cs.add_hyperparameters([n_components, whiten])
json_utils.write_cs_to_json_file(cs, "PCA")
# minimum loss reduction required to make a further partition on a
# leaf node of the tree
gamma = Constant(name="gamma", value=0)
# absolute regularization (in contrast to eta), comparable to
# gradient clipping in deep learning - according to the internet this
# is most important for unbalanced data
max_delta_step = Constant(name="max_delta_step", value=0)
base_score = Constant(name="base_score", value=0.5)
scale_pos_weight = Constant(name="scale_pos_weight", value=1)
cs.add_hyperparameters([
# Active
max_depth,
learning_rate,
n_estimators,
booster,
subsample,
colsample_bytree,
colsample_bylevel,
reg_alpha,
reg_lambda,
# Inactive
min_child_weight,
max_delta_step,
gamma,
base_score,
scale_pos_weight
])
json_utils.write_cs_to_json_file(cs, "XGBRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter
from automl.utl import json_utils
strategy = CategoricalHyperparameter("strategy",
["mean", "median", "most_frequent"],
default_value="mean")
cs = ConfigurationSpace()
cs.add_hyperparameter(strategy)
json_utils.write_cs_to_json_file(cs, "SimpleImputer")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, \
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
n_neighbors = UniformIntegerHyperparameter(name="n_neighbors",
lower=1,
upper=100,
log=True,
default_value=5)
weights = CategoricalHyperparameter(name="weights",
choices=["uniform", "distance"],
default_value="uniform")
p = CategoricalHyperparameter(name="p", choices=[1, 2], default_value=2)
cs.add_hyperparameters([n_neighbors, weights, p])
json_utils.write_cs_to_json_file(cs, "KNeighborsRegressor")
default_value=0)
# probability is no hyperparameter, but an argument to the SVM algo
shrinking = CategoricalHyperparameter(name="shrinking",
choices=["True", "False"],
default_value="True")
tol = UniformFloatHyperparameter(name="tol",
lower=1e-5,
upper=1e-1,
default_value=1e-3,
log=True)
max_iter = Constant("max_iter", -1)
cs = ConfigurationSpace()
cs.add_hyperparameters(
[C, kernel, degree, gamma, coef0, shrinking, tol, max_iter, 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_conditions([
degree_depends_on_kernel, gamma_depends_on_kernel, coef0_depends_on_kernel
])
json_utils.write_cs_to_json_file(cs, "SVR")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter
from automl.utl import json_utils
cs =ConfigurationSpace()
reg_param = UniformFloatHyperparameter('reg_param', 0.0, 1.0,
default_value=0.0)
tol = UniformFloatHyperparameter("tol", 1e-6, 1e-2, default_value=1e-4, log=True)
cs.add_hyperparameter(reg_param)
json_utils.write_cs_to_json_file(cs, "QuadraticDiscriminantAnalysis")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, \
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
criterion = CategoricalHyperparameter(
"criterion", ["gini", "entropy"], default_value="gini")
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter(
"min_samples_split", 0., 1., default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter(
"min_samples_leaf", 0., 0.5, default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0)
max_features = UniformFloatHyperparameter(
"max_features", 0., 1., default_value=0.5)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
cs.add_hyperparameters([criterion, max_features, max_depth,
min_samples_split, min_samples_leaf,
min_weight_fraction_leaf, max_leaf_nodes,
min_impurity_decrease])
json_utils.write_cs_to_json_file(cs, "DecisionTreeRegressor")
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
criterion = CategoricalHyperparameter("criterion", ["gini", "entropy"],
default_value="gini")
max_depth = Constant("max_depth", "None")
min_samples_split = UniformFloatHyperparameter("min_samples_split",
0.,
1.,
default_value=0.5)
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.0)
max_features = UniformFloatHyperparameter("max_features",
0.,
1.,
default_value=0.5)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
cs.add_hyperparameters([
criterion, max_features, max_depth, min_samples_split, min_samples_leaf,
min_weight_fraction_leaf, max_leaf_nodes, min_impurity_decrease
])
json_utils.write_cs_to_json_file(cs, "DecisionTreeClassifier")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, Constant
from automl.utl import json_utils
cs = ConfigurationSpace()
alpha = UniformFloatHyperparameter("alpha",
10**-5,
10.,
log=True,
default_value=1.)
fit_intercept = Constant("fit_intercept", "True")
tol = UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default_value=1e-3,
log=True)
cs.add_hyperparameters([alpha, fit_intercept, tol])
json_utils.write_cs_to_json_file(cs, "Ridge")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
UniformIntegerHyperparameter
from automl.utl import json_utils
cs = ConfigurationSpace()
degree = UniformIntegerHyperparameter("degree", 2, 3, 2)
interaction_only = CategoricalHyperparameter("interaction_only",
["False", "True"], "False")
include_bias = CategoricalHyperparameter("include_bias",
["True", "False"], "True")
cs.add_hyperparameters([degree, interaction_only, include_bias])
json_utils.write_cs_to_json_file(cs, "PolynomialFeatures")
min_samples_leaf = UniformFloatHyperparameter("min_samples_leaf",
0.,
0.5,
default_value=0.0001)
min_weight_fraction_leaf = Constant("min_weight_fraction_leaf", 0.)
subsample = UniformFloatHyperparameter(name="subsample",
lower=0.01,
upper=1.0,
default_value=1.0,
log=False)
max_features = UniformFloatHyperparameter("max_features",
0.,
1.,
default_value=0.5)
max_leaf_nodes = Constant("max_leaf_nodes", "None")
min_impurity_decrease = Constant('min_impurity_decrease', 0.0)
alpha = UniformFloatHyperparameter("alpha",
lower=0.75,
upper=0.99,
default_value=0.9)
cs.add_hyperparameters([
loss, learning_rate, n_estimators, max_depth, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, subsample, max_features,
max_leaf_nodes, min_impurity_decrease, alpha
])
cs.add_condition(InCondition(alpha, loss, ['huber', 'quantile']))
json_utils.write_cs_to_json_file(cs, "GradientBoostingRegressor")
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, \
UnParametrizedHyperparameter, Constant
from automl.utl import json_utils
C = UniformFloatHyperparameter("C", 1e-5, 10, 1.0, log=True)
fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True")
loss = CategoricalHyperparameter("loss", ["hinge", "squared_hinge"],
default_value="hinge")
tol = UniformFloatHyperparameter("tol",
1e-5,
1e-1,
default_value=1e-4,
log=True)
# Note: Average could also be an Integer if > 1
average = CategoricalHyperparameter('average', ['False', 'True'],
default_value='False')
cs = ConfigurationSpace()
cs.add_hyperparameters([loss, fit_intercept, tol, C, average])
json_utils.write_cs_to_json_file(cs, "PassiveAggressiveClassifier")
