(self, pipeline_name=None, astype="lale")
return result.export_to_sklearn_pipeline()
Retrieve one of the trials.

        Parameters
        ----------
        pipeline_name : union type, default None

            - string
                Key for table returned by summary(), return a trainable pipeline.

            - None
                When not specified, return the best trained pipeline found.

        astype : 'lale' or 'sklearn', default 'lale'
            Type of resulting pipeline.

        Returns
        -------
        result : Trained operator if best, trainable operator otherwise.
Retrieve one of the trials.
Retrieve one of the trials .
def get_pipeline(self, pipeline_name=None, astype="lale"):
        """Retrieve one of the trials.

        Parameters
        ----------
        pipeline_name : union type, default None

            - string
                Key for table returned by summary(), return a trainable pipeline.

            - None
                When not specified, return the best trained pipeline found.

        astype : 'lale' or 'sklearn', default 'lale'
            Type of resulting pipeline.

        Returns
        -------
        result : Trained operator if best, trainable operator otherwise."""
        best_name = None
        if self._best_estimator is not None:
            best_name = f'p{self._trials.best_trial["tid"]}'
        if pipeline_name is None:
            pipeline_name = best_name
        if pipeline_name == best_name:
            result = getattr(self, "_best_estimator", None)
        else:
            assert pipeline_name is not None
            tid = int(pipeline_name[1:])
            params = self._trials.trials[tid]["result"]["params"]
            result = create_instance_from_hyperopt_search_space(self.estimator, params)
        if result is None or astype == "lale":
            return result
        assert astype == "sklearn", astype
        return result.export_to_sklearn_pipeline()
def get_pipeline ( self , pipeline_name = None , astype = "lale" ) : best_name = None if self . _best_estimator is not None : best_name = f'p{self._trials.best_trial["tid"]}' if pipeline_name is None : pipeline_name = best_name if pipeline_name == best_name : result = getattr ( self , "_best_estimator" , None ) else : assert pipeline_name is not None tid = int ( pipeline_name [ 1 : ] ) params = self . _trials . trials [ tid ] [ "result" ] [ "params" ] result = create_instance_from_hyperopt_search_space ( self . estimator , params ) if result is None or astype == "lale" : return result assert astype == "sklearn" , astype return result . export_to_sklearn_pipeline ( )