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From Evan Zamir <zamir.e...@gmail.com>
Subject Re: How to add custom steps to Pipeline models?
Date Mon, 15 Aug 2016 04:27:25 GMT
Thanks, but I should have been more clear that I'm trying to do this in
PySpark, not Scala. Using an example I found on SO, I was able to implement
a Pipeline step in Python, but it seems it is more difficult (perhaps
currently impossible) to make it persist to disk (I tried implementing
_to_java method to no avail). Any ideas about that?

On Sun, Aug 14, 2016 at 6:02 PM Jacek Laskowski <jacek@japila.pl> wrote:

> Hi,
>
> It should just work if you followed the Transformer interface [1].
> When you have the transformers, creating a Pipeline is a matter of
> setting them as additional stages (using Pipeline.setStages [2]).
>
> [1]
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
> [2]
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala#L107
>
> Pozdrawiam,
> Jacek Laskowski
> ----
> https://medium.com/@jaceklaskowski/
> Mastering Apache Spark 2.0 http://bit.ly/mastering-apache-spark
> Follow me at https://twitter.com/jaceklaskowski
>
>
> On Fri, Aug 12, 2016 at 9:19 AM, evanzamir <zamir.evan@gmail.com> wrote:
> > I'm building an LDA Pipeline, currently with 4 steps, Tokenizer,
> > StopWordsRemover, CountVectorizer, and LDA. I would like to add more
> steps,
> > for example, stemming and lemmatization, and also 1-gram and 2-grams
> (which
> > I believe is not supported by the default NGram class). Is there a way to
> > add these steps? In sklearn, you can create classes with fit() and
> > transform() methods, and that should be enough. Is that true in Spark ML
> as
> > well (or something similar)?
> >
> >
> >
> > --
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> >
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>

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