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)