def _restore_base_estimators(self, kernel_cache, out, X, cv):
        """Restore custom kernel functions of estimators for predictions"""
        train_folds = {fold: train_index for fold, (train_index, _) in enumerate(cv)}

        for idx, fold, _, est in out:
            if idx in kernel_cache:
                if not hasattr(est, 'fit_X_'):
                    raise ValueError(
                        'estimator %s uses a custom kernel function, '
                        'but does not have the attribute `fit_X_` after training' % self.base_estimators[idx][0])

                est.set_params(kernel=self.base_estimators[idx][1].kernel)
                est.fit_X_ = X[train_folds[fold]]

        return out
def _restore_base_estimators ( self , kernel_cache , out , X , cv ) : train_folds = { fold : train_index for fold , ( train_index , _ ) in enumerate ( cv ) } for idx , fold , _ , est in out : if idx in kernel_cache : if not hasattr ( est , 'fit_X_' ) : raise ValueError ( 'estimator %s uses a custom kernel function, ' 'but does not have the attribute `fit_X_` after training' % self . base_estimators [ idx ] [ 0 ] ) est . set_params ( kernel = self . base_estimators [ idx ] [ 1 ] . kernel ) est . fit_X_ = X [ train_folds [ fold ] ] return out
Restore custom kernel functions of estimators for predictions