Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/3095#discussion_r19846471
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala ---
@@ -700,6 +700,32 @@ object ALS {
* Train a matrix factorization model given an RDD of ratings given by
users to some products,
* in the form of (userID, productID, rating) pairs. We approximate the
ratings matrix as the
* product of two lower-rank matrices of a given rank (number of
features). To solve for these
+ * features, we run a given number of iterations of ALS. This is done
using a level of
+ * parallelism given by `blocks`.
+ *
+ * @param ratings RDD of (userID, productID, rating) pairs
+ * @param rank number of features to use
+ * @param iterations number of iterations of ALS (recommended: 10-20)
+ * @param lambda regularization factor (recommended: 0.01)
+ * @param blocks level of parallelism to split computation into
+ * @param nonnegative whether to enforce nonnegativity
+ */
+ def train(
+ ratings: RDD[Rating],
+ rank: Int,
+ iterations: Int,
+ lambda: Double,
+ blocks: Int,
+ nonnegative: Boolean
+ ): MatrixFactorizationModel = {
+ (new ALS(blocks, blocks, rank, iterations, lambda, false, 1.0)
+ .setNonnegative(nonnegative).run(ratings))
--- End diff --
NonNegative and seed can not be set in same time, do we need to fix this?
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