Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11186#discussion_r52977115
--- Diff: python/pyspark/mllib/recommendation.py ---
@@ -234,11 +238,35 @@ def _prepare(cls, ratings):
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1,
nonnegative=False,
seed=None):
"""
- 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`.
+ 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 `Rating` or (userID, productID, rating) tuple.
+ :param rank:
+ Rank of the feature matrices computed (number of features).
+ :param iterations:
+ Number of iterations run for each batch of data.
--- End diff --
This is a little unclear - what is meant by "for each batch of data"?
Perhaps this should simply be `Number of ALS iterations to run`?
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