if sklearn.__version__ >= "0.24":
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LinearRegression.html
# new: https://scikit-learn.org/0.24/modules/generated/sklearn.linear_model.LinearRegression.html
from lale.schemas import JSON, Bool
LinearRegression = LinearRegression.customize_schema(positive=Bool(
desc="When set to True, forces the coefficients to be positive.",
default=False,
forOptimizer=False,
))
LinearRegression = LinearRegression.customize_schema(constraint=JSON({
"description":
"Setting positive=True is only supported for dense arrays.",
"anyOf": [
{
"type": "object",
"properties": {
"positive": {
"enum": [False]
}
}
},
{
"type": "object",
"laleNot": "X/isSparse"
},
],
}))
lale.docstrings.set_docstrings(LinearRegressionImpl, LinearRegression._schemas)
maxForOptimizer=0.1,
),
)
if sklearn.__version__ >= "0.24":
# old: https://scikit-learn.org/0.22/modules/generated/sklearn.tree.DecisionTreeRegressor.html
# new: https://scikit-learn.org/0.24/modules/generated/sklearn.tree.DecisionTreeRegressor.html
from lale.schemas import JSON
DecisionTreeRegressor = DecisionTreeRegressor.customize_schema(
criterion=JSON({
"description":
"Function to measure the quality of a split.",
"anyOf": [
{
"enum": ["mse", "friedman_mse", "poisson"]
},
{
"enum": ["mae"],
"forOptimizer": False
},
],
"default":
"mse",
}),
presort=None,
)
lale.docstrings.set_docstrings(DecisionTreeRegressorImpl,
DecisionTreeRegressor._schemas)
Nystroem = lale.operators.make_operator(NystroemImpl, _combined_schemas)
if sklearn.__version__ >= "0.24":
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.kernel_approximation.Nystroem.html
# new: https://scikit-learn.org/0.24/modules/generated/sklearn.kernel_approximation.Nystroem.html
from lale.schemas import JSON
Nystroem = Nystroem.customize_schema(n_jobs=JSON({
"anyOf": [
{
"description": "1 unless in joblib.parallel_backend context.",
"enum": [None],
},
{
"description": "Use all processors.",
"enum": [-1]
},
{
"description": "Number of CPU cores.",
"type": "integer",
"minimum": 1,
},
],
"default":
None,
"description":
"The number of jobs to use for the computation.",
}))
lale.docstrings.set_docstrings(NystroemImpl, Nystroem._schemas)
],
desc=
"If bootstrap is True, the number of samples to draw from X to train each base estimator.",
default=None,
),
)
if sklearn.__version__ >= "0.24":
# old: https://scikit-learn.org/0.22/modules/generated/sklearn.tree.ExtraTreesRegressor.html
# new: https://scikit-learn.org/0.24/modules/generated/sklearn.tree.ExtraTreesRegressor.html
from lale.schemas import JSON
ExtraTreesRegressor = ExtraTreesRegressor.customize_schema(criterion=JSON({
"description":
"Function to measure the quality of a split.",
"anyOf": [
{
"enum": ["mse", "friedman_mse", "poisson"]
},
{
"enum": ["mae"],
"forOptimizer": False
},
],
"default":
"mse",
}), )
lale.docstrings.set_docstrings(ExtraTreesRegressorImpl,
ExtraTreesRegressor._schemas)
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.VotingClassifier.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.ensemble.VotingClassifier.html
from lale.schemas import JSON
VotingClassifier = VotingClassifier.customize_schema(estimators=JSON({
"type":
"array",
"items": {
"type":
"array",
"laleType":
"tuple",
"items": [
{
"type": "string"
},
{
"anyOf": [{
"laleType": "operator"
}, {
"enum": [None, "drop"]
}]
},
],
},
"description":
"List of (string, estimator) tuples. Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones.",
}))
lale.docstrings.set_docstrings(VotingClassifierImpl, VotingClassifier._schemas)
VotingClassifier = lale.operators.make_operator(VotingClassifierImpl,
_combined_schemas)
if sklearn.__version__ >= '0.21':
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.VotingClassifier.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.ensemble.VotingClassifier.html
from lale.schemas import JSON
VotingClassifier = VotingClassifier.customize_schema(estimators=JSON({
'type':
'array',
'items': {
'type':
'array',
'laleType':
'tuple',
'items': [{
'type': 'string'
}, {
'anyOf': [{
'laleType': 'operator'
}, {
'enum': [None, 'drop']
}]
}]
},
'description':
'List of (string, estimator) tuples. Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones.'
}))
lale.docstrings.set_docstrings(VotingClassifierImpl, VotingClassifier._schemas)
maxForOptimizer=0.1,
),
)
if sklearn.__version__ >= "0.24":
# old: https://scikit-learn.org/0.22/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
# new: https://scikit-learn.org/0.24/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
from lale.schemas import JSON
GradientBoostingRegressor = GradientBoostingRegressor.customize_schema(
presort=None,
criterion=JSON(
{
"description": "Function to measure the quality of a split.",
"anyOf": [
{"enum": ["mse", "friedman_mse"]},
{
"description": "Deprecated since version 0.24.",
"enum": ["mae"],
"forOptimizer": False,
},
],
"default": "friedman_mse",
}
),
)
lale.docstrings.set_docstrings(
GradientBoostingRegressorImpl, GradientBoostingRegressor._schemas
)