[spark] branch branch-3.3 updated: [SPARK-38816][ML][DOCS] Fix comment about choice of initial factors in ALS

[gu...@apache.org]

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gurwls223 pushed a commit to branch branch-3.3
in repository https://gitbox.apache.org/repos/asf/spark.git


The following commit(s) were added to refs/heads/branch-3.3 by this push:
     new 6d23b40273a [SPARK-38816][ML][DOCS] Fix comment about choice of initial factors in ALS
6d23b40273a is described below

commit 6d23b40273a84c9e65f2d78af312956954e47897
Author: Sean Owen <sr...@gmail.com>
AuthorDate: Mon Apr 18 08:54:45 2022 +0900

    [SPARK-38816][ML][DOCS] Fix comment about choice of initial factors in ALS
    
    ### What changes were proposed in this pull request?
    
    Change a comment in ALS code to match impl. The comment refers to taking the absolute value of a Normal(0,1) value, but it doesn't.
    
    ### Why are the changes needed?
    
    The docs and impl are inconsistent. The current behavior actually seems fine, desirable, so, change the comments.
    
    ### Does this PR introduce _any_ user-facing change?
    
    No.
    
    ### How was this patch tested?
    
    Existing tests
    
    Closes #36228 from srowen/SPARK-38816.
    
    Authored-by: Sean Owen <sr...@gmail.com>
    Signed-off-by: Hyukjin Kwon <gu...@apache.org>
    (cherry picked from commit b2b350b1566b8b45c6dba2f79ccbc2dc4e95816d)
    Signed-off-by: Hyukjin Kwon <gu...@apache.org>
---
 mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index a0ddf7129c9..35c9cca3d7a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -1268,9 +1268,8 @@ object ALS extends DefaultParamsReadable[ALS] with Logging {
       inBlocks: RDD[(Int, InBlock[ID])],
       rank: Int,
       seed: Long): RDD[(Int, FactorBlock)] = {
-    // Choose a unit vector uniformly at random from the unit sphere, but from the
-    // "first quadrant" where all elements are nonnegative. This can be done by choosing
-    // elements distributed as Normal(0,1) and taking the absolute value, and then normalizing.
+    // Choose a unit vector uniformly at random from the unit sphere. This can be done by choosing
+    // elements distributed as Normal(0,1), and then normalizing.
     // This appears to create factorizations that have a slightly better reconstruction
     // (<1%) compared picking elements uniformly at random in [0,1].
     inBlocks.mapPartitions({ iter =>


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