Re: ALS run method versus ALS train versus ALS fit and transform
Yes, just have a look at the method in the source code. It calls "new ALS()run()". It's a convenience wrapper only. On Fri, Jul 17, 2015 at 4:59 PM, Carol McDonald wrote: >>the "new ALS()...run()" form is underneath both of the first two. > > I am not sure what you mean by underneath, so basically the mllib > ALS()...run()does the same thing as the mllib ALS train() ? - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: ALS run method versus ALS train versus ALS fit and transform
>the "new ALS()...run()" form is underneath both of the first two.
I am not sure what you mean by underneath, so basically the mllib ALS()...run()
does the same thing as the mllib ALS train() ?
On Wed, Jul 15, 2015 at 2:02 PM, Sean Owen wrote:
> 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
> 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()
> >
> >
>
Re: ALS run method versus ALS train versus ALS fit and transform
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 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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