Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/460#discussion_r11985249
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala ---
@@ -537,6 +566,34 @@ object ALS {
* 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`, partitioning the data using the
Partitioner `partitioner`.
+ *
+ * @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 seed random seed
+ * @param nonnegative Whether to impose nonnegativity constraints
+ */
+ def train(
+ ratings: RDD[Rating],
+ rank: Int,
+ iterations: Int,
+ lambda: Double,
+ blocks: Int,
+ seed: Long,
+ nonnegative: Boolean) = {
--- End diff --
Those static methods shouldn't be used for more than 3 parameters. It is
extremely hard to remember the order. Even with docs, they are still not user
friendly. I'm actually okay with removing this static method and using the
builder pattern to create an ALS instance.
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