(self, pipelines)
return self.results.to_dataframe(pipelines=pipelines)
Runs all pipelines on all datasets.

        This function will apply all provided pipelines and return a dataframe
        containing the results of the evaluation.

        Parameters
        ----------
        pipelines : dict of pipeline instance.
            A dict containing the sklearn pipeline to evaluate.

        Returns
        -------
        results: pd.DataFrame
            A dataframe containing the results.
Runs all pipelines on all datasets.
Runs all pipelines on all datasets .
def process(self, pipelines):
        """Runs all pipelines on all datasets.

        This function will apply all provided pipelines and return a dataframe
        containing the results of the evaluation.

        Parameters
        ----------
        pipelines : dict of pipeline instance.
            A dict containing the sklearn pipeline to evaluate.

        Returns
        -------
        results: pd.DataFrame
            A dataframe containing the results.

        """

        # check pipelines
        if not isinstance(pipelines, dict):
            raise (ValueError("pipelines must be a dict"))

        for _, pipeline in pipelines.items():
            if not (isinstance(pipeline, BaseEstimator)):
                raise (ValueError("pipelines must only contains Pipelines " "instance"))

        for dataset in self.datasets:
            log.info("Processing dataset: {}".format(dataset.code))
            results = self.evaluate(dataset, pipelines)
            for res in results:
                self.push_result(res, pipelines)

        return self.results.to_dataframe(pipelines=pipelines)
def process ( self , pipelines ) : # check pipelines if not isinstance ( pipelines , dict ) : raise ( ValueError ( "pipelines must be a dict" ) ) for _ , pipeline in pipelines . items ( ) : if not ( isinstance ( pipeline , BaseEstimator ) ) : raise ( ValueError ( "pipelines must only contains Pipelines " "instance" ) ) for dataset in self . datasets : log . info ( "Processing dataset: {}" . format ( dataset . code ) ) results = self . evaluate ( dataset , pipelines ) for res in results : self . push_result ( res , pipelines ) return self . results . to_dataframe ( pipelines = pipelines )