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new 6dff114 [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec
when numIterations are large
6dff114 is described below
commit 6dff114ddb3de7877625b79bea818ba724ccd22d
Author: Liang-Chi Hsieh <liang...@uber.com>
AuthorDate: Thu Dec 5 16:32:33 2019 -0800
[SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when
numIterations are large
### What changes were proposed in this pull request?
This patch adds normalization to word vectors when fitting dataset in
Word2Vec.
### Why are the changes needed?
Running Word2Vec on some datasets, when numIterations is large, can produce
infinity word vectors.
### Does this PR introduce any user-facing change?
Yes. After this patch, Word2Vec won't produce infinity word vectors.
### How was this patch tested?
Manually. This issue is not always reproducible on any dataset. The dataset
known to reproduce it is too large (925M) to upload.
```scala
case class Sentences(name: String, words: Array[String])
val dataset = spark.read
.option("header", "true").option("sep", "\t")
.option("quote", "").option("nullValue", "\\N")
.csv("/tmp/title.akas.tsv")
.filter("region = 'US' or language = 'en'")
.select("title")
.as[String]
.map(s => Sentences(s, s.split(' ')))
.persist()
println("Training model...")
val word2Vec = new Word2Vec()
.setInputCol("words")
.setOutputCol("vector")
.setVectorSize(64)
.setWindowSize(4)
.setNumPartitions(50)
.setMinCount(5)
.setMaxIter(30)
val model = word2Vec.fit(dataset)
model.getVectors.show()
```
Before:
```
Training model...
+-------------+--------------------+
| word| vector|
+-------------+--------------------+
| Unspoken|[-Infinity,-Infin...|
| Talent|[-Infinity,Infini...|
| Hourglass|[2.02805806500023...|
|Nickelodeon's|[-4.2918617120906...|
| Priests|[-1.3570403355926...|
| Religion:|[-6.7049072282803...|
| Bu|[5.05591774315586...|
| Totoro:|[-1.0539840178632...|
| Trouble,|[-3.5363592836003...|
| Hatter|[4.90413981352826...|
| '79|[7.50436471285412...|
| Vile|[-2.9147142985312...|
| 9/11|[-Infinity,Infini...|
| Santino|[1.30005911270850...|
| Motives|[-1.2538958306253...|
| '13|[-4.5040152427657...|
| Fierce|[Infinity,Infinit...|
| Stover|[-2.6326895394029...|
| 'It|[1.66574533864436...|
| Butts|[Infinity,Infinit...|
+-------------+--------------------+
only showing top 20 rows
```
After:
```
Training model...
+-------------+--------------------+
| word| vector|
+-------------+--------------------+
| Unspoken|[-0.0454501919448...|
| Talent|[-0.2657704949378...|
| Hourglass|[-0.1399687677621...|
|Nickelodeon's|[-0.1767119318246...|
| Priests|[-0.0047509293071...|
| Religion:|[-0.0411605164408...|
| Bu|[0.11837736517190...|
| Totoro:|[0.05258282646536...|
| Trouble,|[0.09482011198997...|
| Hatter|[0.06040831282734...|
| '79|[0.04783720895648...|
| Vile|[-0.0017210749210...|
| 9/11|[-0.0713915303349...|
| Santino|[-0.0412711687386...|
| Motives|[-0.0492418706417...|
| '13|[-0.0073119504377...|
| Fierce|[-0.0565455369651...|
| Stover|[0.06938160210847...|
| 'It|[0.01117012929171...|
| Butts|[0.05374567210674...|
+-------------+--------------------+
only showing top 20 rows
```
Closes #26722 from viirya/SPARK-24666-2.
Lead-authored-by: Liang-Chi Hsieh <liang...@uber.com>
Co-authored-by: Liang-Chi Hsieh <vii...@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liang...@uber.com>
(cherry picked from commit 755d8894485396b0a21304568c8ec5a55030f2fd)
Signed-off-by: Liang-Chi Hsieh <liang...@uber.com>
---
.../scala/org/apache/spark/mllib/feature/Word2Vec.scala | 17 ++++++++++++++---
.../org/apache/spark/ml/feature/Word2VecSuite.scala | 8 --------
2 files changed, 14 insertions(+), 11 deletions(-)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index d5b91df..bb5d02e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -438,9 +438,20 @@ class Word2Vec extends Serializable with Logging {
}
}.flatten
}
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val synAgg = partial.mapPartitions { iter =>
+ iter.map { case (id, vec) =>
+ (id, (vec, 1))
+ }
+ }.reduceByKey { case ((v1, count1), (v2, count2)) =>
+ blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
+ (v1, count1 + count2)
+ }.map { case (id, (vec, count)) =>
+ blas.sscal(vectorSize, 1.0f / count, vec, 1)
+ (id, vec)
}.collect()
var i = 0
while (i < synAgg.length) {
diff --git
a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index b59c4e7..c816a6c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -75,14 +75,6 @@ class Word2VecSuite extends MLTest with DefaultReadWriteTest
{
test("getVectors") {
val sentence = "a b " * 100 + "a c " * 10
val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split("
"))
-
- val codes = Map(
- "a" -> Array(-0.2811822295188904, -0.6356269121170044,
-0.3020961284637451),
- "b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342),
- "c" -> Array(-0.08456747233867645, 0.5137411952018738,
0.11731560528278351)
- )
- val expectedVectors = codes.toSeq.sortBy(_._1).map { case (w, v) =>
Vectors.dense(v) }
-
val docDF = doc.zip(doc).toDF("text", "alsotext")
val model = new Word2Vec()
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