The first two examples are from the .mllib API. Really, the "new
ALS()...run()" form is underneath both of the first two. In the second
case, you're calling a convenience method that calls something similar
to the first example.
The second example is from the new .ml "pipelines" API. Similar ideas,
but a different API.
On Wed, Jul 15, 2015 at 9:55 PM, Carol McDonald <cmcdon...@maprtech.com> wrote:
> In the Spark mllib examples MovieLensALS.scala ALS run is used, however in
> the movie recommendation with mllib tutorial ALS train is used , What is the
> difference, when should you use one versus the other
>
> val model = new ALS()
> .setRank(params.rank)
> .setIterations(params.numIterations)
> .setLambda(params.lambda)
> .setImplicitPrefs(params.implicitPrefs)
> .setUserBlocks(params.numUserBlocks)
> .setProductBlocks(params.numProductBlocks)
> .run(training)
>
>
> val model = ALS.train(training, rank, numIter, lambda)
>
> Also in org.apache.spark.examples.ml , fit and transform is used. Which one
> do you recommend using ?
>
> val als = new ALS()
> .setUserCol("userId")
> .setItemCol("movieId")
> .setRank(params.rank)
> .setMaxIter(params.maxIter)
> .setRegParam(params.regParam)
> .setNumBlocks(params.numBlocks)
>
> val model = als.fit(training.toDF())
>
> val predictions = model.transform(test.toDF()).cache()
>
>
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