(
        self, X, y=None, optimizer=None, cv=None, scoring=None, **kwargs
    )
return trained.get_pipeline()
Perform combined algorithm selection and hyperparameter tuning on this planned operator.

        Parameters
        ----------
        X:
            Features that conform to the X property of input_schema_fit.
        y: optional
            Labels that conform to the y property of input_schema_fit.
            Default is None.
        optimizer:
            lale.lib.lale.Hyperopt or lale.lib.lale.GridSearchCV
            default is None.
        cv:
            cross-validation option that is valid for the optimizer.
            Default is None, which will use the optimizer's default value.
        scoring:
            scoring option that is valid for the optimizer.
            Default is None, which will use the optimizer's default value.
        kwargs:
            Other keyword arguments to be passed to the optimizer.

        Returns
        -------
        TrainableOperator
            Best operator discovered by the optimizer.
Perform combined algorithm selection and hyperparameter tuning on this planned operator.
Perform combined algorithm selection and hyperparameter tuning on this planned operator .
def auto_configure(
        self, X, y=None, optimizer=None, cv=None, scoring=None, **kwargs
    ) -> "TrainedOperator":
        """
        Perform combined algorithm selection and hyperparameter tuning on this planned operator.

        Parameters
        ----------
        X:
            Features that conform to the X property of input_schema_fit.
        y: optional
            Labels that conform to the y property of input_schema_fit.
            Default is None.
        optimizer:
            lale.lib.lale.Hyperopt or lale.lib.lale.GridSearchCV
            default is None.
        cv:
            cross-validation option that is valid for the optimizer.
            Default is None, which will use the optimizer's default value.
        scoring:
            scoring option that is valid for the optimizer.
            Default is None, which will use the optimizer's default value.
        kwargs:
            Other keyword arguments to be passed to the optimizer.

        Returns
        -------
        TrainableOperator
            Best operator discovered by the optimizer.
        """
        if optimizer is None:
            raise ValueError("Please provide a valid optimizer for auto_configure.")
        if kwargs is None:
            kwargs = {}
        if cv is not None:
            kwargs["cv"] = cv
        if scoring is not None:
            kwargs["scoring"] = scoring
        optimizer_obj = optimizer(estimator=self, **kwargs)
        trained = optimizer_obj.fit(X, y)
        return trained.get_pipeline()
def auto_configure ( self , X , y = None , optimizer = None , cv = None , scoring = None , * * kwargs ) -> "TrainedOperator" : if optimizer is None : raise ValueError ( "Please provide a valid optimizer for auto_configure." ) if kwargs is None : kwargs = { } if cv is not None : kwargs [ "cv" ] = cv if scoring is not None : kwargs [ "scoring" ] = scoring optimizer_obj = optimizer ( estimator = self , * * kwargs ) trained = optimizer_obj . fit ( X , y ) return trained . get_pipeline ( )