that's what I was looking for, thanks.  Just distributed predicts, not
parallel inside predict.  I'll check out the imap_easy() function.  Sounds
like it isn't appropriate to build into the code.

thanks,

-Louis


On Thu, May 29, 2014 at 6:15 AM, dkrasner <notifications@github.com> wrote:

> In rosetta/text/vw_helpers.py:
>
> > @@ -468,7 +469,7 @@ def predict(
> >
> >          counts = Counter(tokenized_text)
> >          counts = pd.Series(
> > -            {k: counts[k] for k in counts if k in set(self.tokens)}
>
> agreed with Ian; if you are thinking of parallelizing over the tokens
> list, then it is as a Ian says. If you are interested in making many
> predictions, i.e. say over a large collection of tokenized text, then
> potentially this can be worked into predict() or added as another method;
> however, I am not convinced this should be handled by the LDAResults class,
> since a user can simply take imap_easy() from the parallel module and wrap
> .predict() to parallelize over the collection. Thoughts?...
>
> —
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