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 <ja...@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.e...@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)? > > > > > > > > -- > > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/How-to-add-custom-steps-to-Pipeline-models-tp27522.html > > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > > > --------------------------------------------------------------------- > > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > > >
