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
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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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