def build_config_space(clustering_ls=["KMeans", "DBSCAN"],
dim_reduction_ls=[]):
cs = ConfigurationSpace()
if len(clustering_ls) > 0:
clustering_choice = CategoricalHyperparameter(
"clustering_choice", clustering_ls, default_value=clustering_ls[0])
cs.add_hyperparameters([clustering_choice])
if len(dim_reduction_ls) > 0:
dim_reduction_choice = CategoricalHyperparameter(
"dim_reduction_choice",
dim_reduction_ls,
default_value=dim_reduction_ls[0])
cs.add_hyperparameters([dim_reduction_choice])
for idx, string in enumerate(
itertools.chain(clustering_ls, dim_reduction_ls)):
algorithm = Mapper.getClass(string)
# encode parameter names
encoded_params = []
for param in algorithm.params:
encoded_string = StringUtils.encode_parameter(
param.name, algorithm.name)
param.name = encoded_string
# add encoded paramters to configuration space
cs.add_hyperparameters(algorithm.params)
# define dependency
for param in algorithm.params:
cs.add_condition(
InCondition(child=param,
parent=clustering_choice if
idx < len(clustering_ls) else dim_reduction_choice,
values=[string]))
# add forbidden clauses
for condition in algorithm.forbidden_clauses:
cs.add_forbidden_clause(condition)
return cs
def build_config_space(clustering_ls: List[str] = None,
feature_selection_ls: List[str] = None):
if clustering_ls is None:
clustering_ls = ["KMeans", "DBSCAN"]
if feature_selection_ls is None:
feature_selection_ls = ["NullModel", "NormalizedCut"]
assert len(clustering_ls) > 0
assert len(feature_selection_ls) > 0
cs = ConfigurationSpace()
clustering_choice = CategoricalHyperparameter(
"clustering_choice", clustering_ls, default_value=clustering_ls[0])
feature_selection_choice = CategoricalHyperparameter(
"feature_selection_choice",
feature_selection_ls,
default_value=feature_selection_ls[0])
cs.add_hyperparameters([clustering_choice])
cs.add_hyperparameters([feature_selection_choice])
for idx, string in enumerate(
itertools.chain(clustering_ls, feature_selection_ls)):
algorithm = Mapper.get_class(string)
for param in algorithm.params:
encoded_string = encode_parameter(param.name, algorithm.name)
param.name = encoded_string
cs.add_hyperparameters(algorithm.params)
for param in algorithm.params:
cs.add_condition(
InCondition(
child=param,
parent=clustering_choice
if idx < len(clustering_ls) else feature_selection_choice,
values=[string]))
for condition in algorithm.forbidden_clauses:
cs.add_forbidden_clause(condition)
return cs
