return self._wrapped_model.predict_proba(X)
DecisionTreeClassifier: lale.operators.IndividualOp
DecisionTreeClassifier = lale.operators.make_operator(
DecisionTreeClassifierImpl, _combined_schemas)
if sklearn.__version__ >= "0.22":
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.tree.DecisionTreeClassifier.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.tree.DecisionTreeClassifier.html
from lale.schemas import AnyOf, Bool, Enum, Float
DecisionTreeClassifier = DecisionTreeClassifier.customize_schema(
presort=AnyOf(
types=[Bool(), Enum(["deprecated"])],
desc="This parameter is deprecated and will be removed in v0.24.",
default="deprecated",
),
ccp_alpha=Float(
desc=
"Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed.",
default=0.0,
forOptimizer=True,
min=0.0,
maxForOptimizer=0.1,
),
)
lale.docstrings.set_docstrings(DecisionTreeClassifierImpl,
DecisionTreeClassifier._schemas)
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
# new: https://scikit-learn.org/0.22/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
from lale.schemas import AnyOf, Float, Int, Null
ExtraTreesRegressor = ExtraTreesRegressor.customize_schema(
n_estimators=Int(
desc="The number of trees in the forest.",
default=100,
forOptimizer=True,
minForOptimizer=10,
maxForOptimizer=100,
),
ccp_alpha=Float(
desc=
"Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed.",
default=0.0,
forOptimizer=False,
min=0.0,
maxForOptimizer=0.1,
),
max_samples=AnyOf(
types=[
Null(desc="Draw X.shape[0] samples."),
Int(desc="Draw max_samples samples.", min=1),
Float(
desc="Draw max_samples * X.shape[0] samples.",
min=0.0,
exclusiveMin=True,
max=1.0,
exclusiveMax=True,
),
],
SVC: lale.operators.PlannedIndividualOp
SVC = lale.operators.make_operator(sklearn.svm.SVC, _combined_schemas)
if sklearn.__version__ >= "0.22":
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.svm.SVC.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.svm.SVC.html
from lale.schemas import AnyOf, Bool, Enum, Float
SVC = SVC.customize_schema(
gamma=AnyOf(
types=[
Enum(["scale", "auto"]),
Float(
minimum=0.0,
exclusiveMinimum=True,
minimumForOptimizer=3.0517578125e-05,
maximumForOptimizer=8,
distribution="loguniform",
),
],
desc="Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.",
default="scale",
),
break_ties=Bool(
desc="If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned.",
default=False,
),
set_as_available=True,
)
"output_transform": _output_transform_schema,
},
}
FeatureAgglomeration: lale.operators.PlannedIndividualOp
FeatureAgglomeration = lale.operators.make_operator(
sklearn.cluster.FeatureAgglomeration, _combined_schemas)
if sklearn.__version__ >= "0.21":
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.cluster.FeatureAgglomeration.html
# new: https://scikit-learn.org/0.21/modules/generated/sklearn.cluster.FeatureAgglomeration.html
from lale.schemas import AnyOf, Enum, Float, Int, Null, Object
FeatureAgglomeration = FeatureAgglomeration.customize_schema(
distance_threshold=AnyOf(
types=[Float(), Null()],
desc=
"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.",
),
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,
),
fill_values=AnyOf(
types=[
Array(
items=AnyOf(
types=[Float(), String(), Enum(values=[np.nan]), Null()]
)
),
Null(),
],
desc="The placeholder for fill values used in constants strategy",
default=None,
),
default="l2",
),
),
)
if sklearn.__version__ >= "0.22":
# old: https://scikit-learn.org/0.21/modules/generated/sklearn.linear_model.LogisticRegression.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.LogisticRegression.html
LogisticRegression = typing.cast(
lale.operators.PlannedIndividualOp,
LogisticRegression.customize_schema(
solver=Enum(
values=["newton-cg", "lbfgs", "liblinear", "sag", "saga"],
desc="Algorithm for optimization problem.",
default="lbfgs",
),
multi_class=Enum(
values=["auto", "ovr", "multinomial"],
desc="If the option chosen is `ovr`, then a binary problem is fit for each label. For `multinomial` the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. `multinomial` is unavailable when solver=`liblinear`. `auto` selects `ovr` if the data is binary, or if solver=`liblinear`, and otherwise selects `multinomial`.",
default="auto",
),
l1_ratio=AnyOf(
types=[Float(min=0.0, max=1.0), Null()],
desc="The Elastic-Net mixing parameter.",
default=None,
),
),
)
lale.docstrings.set_docstrings(LogisticRegressionImpl, LogisticRegression._schemas)
default="both",
forOptimizer=True,
),
set_as_available=True,
)
if sklearn.__version__ >= "1.0":
# old: https://scikit-learn.org/0.24/modules/generated/sklearn.decomposition.NMF.html
# new: https://scikit-learn.org/1.0/modules/generated/sklearn.decomposition.NMF.html
from lale.schemas import AnyOf, Enum, Float, Null
NMF = NMF.customize_schema(
alpha=Float(
desc="""Constant that multiplies the regularization terms.
Set it to zero to have no regularization. When using alpha instead of alpha_W and alpha_H,
the regularization terms are not scaled by the n_features (resp. n_samples) factors for W (resp. H).""",
default=0.0,
forOptimizer=False,
),
alpha_W=Float(
desc="""Constant that multiplies the regularization terms of W. Set it to zero (default) to have no regularization on W.""",
minimumForOptimizer=1e-10,
maximumForOptimizer=1.0,
distribution="loguniform",
default=0.0,
forOptimizer=True,
),
alpha_H=AnyOf(
types=[
Enum(values=["same"]),
Float(
)
if sklearn.__version__ >= "0.22":
# old: https://scikit-learn.org/0.21/modules/generated/sklearn.linear_model.LogisticRegression.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.LogisticRegression.html
LogisticRegression = typing.cast(
lale.operators.PlannedIndividualOp,
LogisticRegression.customize_schema(
solver=Enum(
values=["newton-cg", "lbfgs", "liblinear", "sag", "saga"],
desc="Algorithm for optimization problem.",
default="lbfgs",
),
multi_class=Enum(
values=["auto", "ovr", "multinomial"],
desc=
"If the option chosen is `ovr`, then a binary problem is fit for each label. For `multinomial` the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. `multinomial` is unavailable when solver=`liblinear`. `auto` selects `ovr` if the data is binary, or if solver=`liblinear`, and otherwise selects `multinomial`.",
default="auto",
),
l1_ratio=AnyOf(
types=[Float(minimum=0.0, maximum=1.0),
Null()],
desc="The Elastic-Net mixing parameter.",
default=None,
),
set_as_available=True,
),
)
lale.docstrings.set_docstrings(LogisticRegression)
)
if sklearn.__version__ >= "0.22":
# old: https://scikit-learn.org/0.21/modules/generated/sklearn.linear_model.LogisticRegression.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.LogisticRegression.html
LogisticRegression = typing.cast(
lale.operators.PlannedIndividualOp,
LogisticRegression.customize_schema(
solver=Enum(
values=["newton-cg", "lbfgs", "liblinear", "sag", "saga"],
desc="Algorithm for optimization problem.",
default="lbfgs",
),
multi_class=Enum(
values=["auto", "ovr", "multinomial"],
desc=
"If the option chosen is `ovr`, then a binary problem is fit for each label. For `multinomial` the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. `multinomial` is unavailable when solver=`liblinear`. `auto` selects `ovr` if the data is binary, or if solver=`liblinear`, and otherwise selects `multinomial`.",
default="auto",
),
l1_ratio=AnyOf(
types=[Float(min=0.0, max=1.0),
Null()],
desc="The Elastic-Net mixing parameter.",
default=None,
),
),
)
lale.docstrings.set_docstrings(LogisticRegressionImpl,
LogisticRegression._schemas)
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,
),
fill_values=AnyOf(
types=[
Array(items=AnyOf(types=[
Float(),
String(),
Enum(values=[np.nan]),
Null()
])),
return self._wrapped_model.decision_function(X)
LogisticRegression = lale.operators.make_operator(LogisticRegressionImpl,
_combined_schemas)
if sklearn.__version__ >= '0.22':
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LogisticRegression.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.LogisticRegression.html
from lale.schemas import AnyOf, Enum, Float, Null
import typing
LogisticRegression = typing.cast(
lale.operators.PlannedIndividualOp,
LogisticRegression.customize_schema(
solver=Enum(
values=['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
desc='Algorithm for optimization problem.',
default='lbfgs'),
multi_class=Enum(
values=['auto', 'ovr', 'multinomial'],
desc=
'If the option chosen is `ovr`, then a binary problem is fit for each label. For `multinomial` the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. `multinomial` is unavailable when solver=`liblinear`. `auto` selects `ovr` if the data is binary, or if solver=`liblinear`, and otherwise selects `multinomial`.',
default='auto'),
l1_ratio=AnyOf(types=[Float(min=0.0, max=1.0),
Null()],
desc='The Elastic-Net mixing parameter.',
default=None)))
lale.docstrings.set_docstrings(LogisticRegressionImpl,
LogisticRegression._schemas)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
LogisticRegression = lale.operators.make_operator(LogisticRegressionImpl, _combined_schemas)
if sklearn.__version__ >= '0.22':
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.LogisticRegression.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.LogisticRegression.html
from lale.schemas import AnyOf, Enum, Float, Null
import typing
LogisticRegression = typing.cast(lale.operators.PlannedIndividualOp, LogisticRegression.customize_schema(
solver=Enum(
values=['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
desc='Algorithm for optimization problem.',
default='lbfgs'),
multi_class=Enum(
values=['auto', 'ovr', 'multinomial'],
desc='If the option chosen is `ovr`, then a binary problem is fit for each label. For `multinomial` the loss minimised is the multinomial loss fit across the entire probability distribution, even when the data is binary. `multinomial` is unavailable when solver=`liblinear`. `auto` selects `ovr` if the data is binary, or if solver=`liblinear`, and otherwise selects `multinomial`.',
default='auto'),
l1_ratio=AnyOf(
types=[
Float(min=0.0, max=1.0),
Null()],
desc='The Elastic-Net mixing parameter.',
default=None)))
lale.docstrings.set_docstrings(LogisticRegressionImpl, LogisticRegression._schemas)
'output_predict_proba': _output_predict_proba_schema,
'input_decision_function': _input_decision_function_schema,
'output_decision_function': _output_decision_function_schema
}
}
SVC: lale.operators.IndividualOp
SVC = lale.operators.make_operator(SVCImpl, _combined_schemas)
if sklearn.__version__ >= '0.22':
# old: https://scikit-learn.org/0.20/modules/generated/sklearn.svm.SVC.html
# new: https://scikit-learn.org/0.23/modules/generated/sklearn.svm.SVC.html
from lale.schemas import AnyOf, Bool, Enum, Float
SVC = SVC.customize_schema(
gamma=AnyOf(types=[
Enum(['scale', 'auto']),
Float(min=0.0,
exclusiveMin=True,
minForOptimizer=3.0517578125e-05,
maxForOptimizer=8,
distribution='loguniform')
],
desc="Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.",
default='scale'),
break_ties=Bool(
desc=
"If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned.",
default=False))
lale.docstrings.set_docstrings(SVCImpl, SVC._schemas)
