(self)
Performs batch prediction.

        This technically invokes predict_pp underneath.

        Returns a List[List[Tuple]] where each List[Tuple] corresponds to
        a cell (ordered by the order a cell appears in `self.domain_df`
        during construction) and each Tuple is (val, proba) where
        val is the domain value and proba is the estimator's posterior probability estimate.
Performs batch prediction.
Performs batch prediction .
def predict_pp_batch(self):
        """
        Performs batch prediction.

        This technically invokes predict_pp underneath.

        Returns a List[List[Tuple]] where each List[Tuple] corresponds to
        a cell (ordered by the order a cell appears in `self.domain_df`
        during construction) and each Tuple is (val, proba) where
        val is the domain value and proba is the estimator's posterior probability estimate.
        """
        for row in tqdm(self.domain_df.to_records()):
            yield self.predict_pp(self._raw_records_by_tid[row['_tid_']], row['attribute'], row['domain'].split('|||'))
def predict_pp_batch ( self ) : for row in tqdm ( self . domain_df . to_records ( ) ) : yield self . predict_pp ( self . _raw_records_by_tid [ row [ '_tid_' ] ] , row [ 'attribute' ] , row [ 'domain' ] . split ( '|||' ) )