def _run_algorithm(self):
        """ Runs nearest neighbor (NN) identification and feature scoring to yield MultiSURF scores. """
        nan_entries = np.isnan(self._X)

        NNlist = [self._find_neighbors(datalen) for datalen in range(self._datalen)]

        scores = np.sum(Parallel(n_jobs=self.n_jobs)(delayed(
            MultiSURF_compute_scores)(instance_num, self.attr, nan_entries, self._num_attributes, self.mcmap,
                                      NN_near, self._headers, self._class_type, self._X, self._y, self._labels_std, self.data_type)
            for instance_num, NN_near in zip(range(self._datalen), NNlist)), axis=0)

        return np.array(scores)
def _run_algorithm ( self ) : nan_entries = np . isnan ( self . _X ) NNlist = [ self . _find_neighbors ( datalen ) for datalen in range ( self . _datalen ) ] scores = np . sum ( Parallel ( n_jobs = self . n_jobs ) ( delayed ( MultiSURF_compute_scores ) ( instance_num , self . attr , nan_entries , self . _num_attributes , self . mcmap , NN_near , self . _headers , self . _class_type , self . _X , self . _y , self . _labels_std , self . data_type ) for instance_num , NN_near in zip ( range ( self . _datalen ) , NNlist ) ) , axis = 0 ) return np . array ( scores )
Runs nearest neighbor (NN) identification and feature scoring to yield MultiSURF scores.