How to manually iterate a sorter

So I'm working on implementing a recipe that will periodically fire off retraining batch jobs and reload the trained model in a modified textcat.teach recipe.

When I detect the presence of a newly trained model I want to create a new sorter and feed it with newly scored examples and then yield those to the UI. I'd like to use the prefer_uncertain sorter and yield its output from my custom sorter.

roughly something like this:

class ModelSwappingIterator:


def __call__(self, stream, batch_size=None):
  sorted_stream = prefer_uncertain(self.model(stream))
  while True:
    if self.new_model_exists():
      self.swap_model()  # newly trained self.model
      sorted_stream = prefer_uncertain(self.model(stream))
    yield next(sorted_stream)      

Whenever I try something like this I am told that ExpMovingAverage is not an iterator. If I try to call it instead I'm told that it's not callable.

Is there any way I can manually pull sorted examples out of the sorters?

The sorters all yield example dicts, so you should be able to just yield from it / loop over it like this:

for eg in sorted_stream:
    yield eg
yield from sorted_stream