"The linkage distance threshold above which, clusters will not be merged.",
default=None,
),
n_clusters=AnyOf(
types=[
Int(minForOptimizer=2,
maxForOptimizer=8,
laleMaximum="X/maxItems"),
Null(forOptimizer=False),
],
default=2,
forOptimizer=False,
desc="The number of clusters to find.",
),
constraint=AnyOf(
[Object(n_clusters=Null()),
Object(distance_threshold=Null())],
desc="n_clusters must be None if distance_threshold is not None.",
),
)
FeatureAgglomeration = FeatureAgglomeration.customize_schema(
constraint=AnyOf(
[
Object(compute_full_tree=Enum(["True"])),
Object(distance_threshold=Null()),
],
desc=
"compute_full_tree must be True if distance_threshold is not None.",
))
if sklearn.__version__ >= "0.24":
from packaging import version
from lale.schemas import AnyOf, Array, Enum, Float, Not, Null, Object, String
autoai_libs_version = version.parse(autoai_libs_version_str)
if autoai_libs_version >= version.Version("1.12.18"):
NumImputer = typing.cast(
lale.operators.PlannedIndividualOp,
NumImputer.customize_schema(
set_as_available=True,
constraint=[
AnyOf(
desc="fill_value and fill_values cannot both be specified",
forOptimizer=False,
types=[Object(fill_value=Null()), Object(fill_values=Null())],
),
AnyOf(
desc="if strategy=constants, the fill_values cannot be None",
forOptimizer=False,
types=[
Object(strategy=Not(Enum(["constants"]))),
Not(Object(fill_values=Null())),
],
),
],
fill_value=AnyOf(
types=[Float(), String(), Enum(values=[np.nan]), Null()],
desc="The placeholder for fill value used in constant strategy",
default=None,
),