Hi Roberto, There are two ALS available: ml.recommendation.ALS <http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation> and mllib.recommendation.ALS <http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.recommendation> . They have different usage and methods. I know it's confusion that Spark provide two version of the same algorithm. I strongly recommend to use the ALS algorithm at ML package.
Yanbo 2015-12-04 1:31 GMT+08:00 Felix Cheung <felixcheun...@hotmail.com>: > Please open an issue in JIRA, thanks! > > > > > > On Thu, Dec 3, 2015 at 3:03 AM -0800, "Roberto Pagliari" < > roberto.pagli...@asos.com> wrote: > > Hello, > I believe there is a mismatch between the API documentation (1.5.2) and > the software currently available. > > Not all functions mentioned here > > http://spark.apache.org/docs/latest/api/python/pyspark.ml.html#module-pyspark.ml.recommendation > > are, in fact available. For example, the code below from the tutorial works > > # Build the recommendation model using Alternating Least Squaresrank = > 10numIterations = 10model = ALS.train(ratings, rank, numIterations) > > > While the alternative shown in the API documentation will not (it will > complain that ALS takes no arguments. Also, but inspecting the module with > Python utilities I could not find several methods mentioned in the API docs) > > >>> df = sqlContext.createDataFrame(... [(0, 0, 4.0), (0, 1, 2.0), (1, 1, > >>> 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],... ["user", "item", > >>> "rating"])>>> als = ALS(rank=10, maxIter=5)>>> model = als.fit(df) > > > > Thank you, > >
