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author | Yuhao Yang <hhbyyh@gmail.com> | 2016-03-17 11:21:11 +0200 |
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committer | Nick Pentreath <nick.pentreath@gmail.com> | 2016-03-17 11:21:11 +0200 |
commit | 357d82d84d6372debd28da6ad0a2ee904957a7fe (patch) | |
tree | 1c0facd6a63b865b7ea06ff516f69bf479a26cba /mllib | |
parent | 204c9dec2c3876d20558ef5bda4dbd6edaf59643 (diff) | |
download | spark-357d82d84d6372debd28da6ad0a2ee904957a7fe.tar.gz spark-357d82d84d6372debd28da6ad0a2ee904957a7fe.tar.bz2 spark-357d82d84d6372debd28da6ad0a2ee904957a7fe.zip |
[SPARK-13629][ML] Add binary toggle Param to CountVectorizer
## What changes were proposed in this pull request?
It would be handy to add a binary toggle Param to CountVectorizer, as in the scikit-learn one: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
If set, then all non-zero counts will be set to 1.
## How was this patch tested?
unit tests
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #11536 from hhbyyh/cvToggle.
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala | 29 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala | 19 |
2 files changed, 46 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala index f7d08b39a9..a3845d3977 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala @@ -206,6 +206,27 @@ class CountVectorizerModel(override val uid: String, val vocabulary: Array[Strin /** @group setParam */ def setMinTF(value: Double): this.type = set(minTF, value) + /** + * Binary toggle to control the output vector values. + * If True, all non zero counts are set to 1. This is useful for discrete probabilistic + * models that model binary events rather than integer counts + * + * Default: false + * @group param + */ + val binary: BooleanParam = + new BooleanParam(this, "binary", "If True, all non zero counts are set to 1. " + + "This is useful for discrete probabilistic models that model binary events rather " + + "than integer counts") + + /** @group getParam */ + def getBinary: Boolean = $(binary) + + /** @group setParam */ + def setBinary(value: Boolean): this.type = set(binary, value) + + setDefault(binary -> false) + /** Dictionary created from [[vocabulary]] and its indices, broadcast once for [[transform()]] */ private var broadcastDict: Option[Broadcast[Map[String, Int]]] = None @@ -232,7 +253,13 @@ class CountVectorizerModel(override val uid: String, val vocabulary: Array[Strin } else { tokenCount * minTf } - Vectors.sparse(dictBr.value.size, termCounts.filter(_._2 >= effectiveMinTF).toSeq) + val effectiveCounts = if ($(binary)) { + termCounts.filter(_._2 >= effectiveMinTF).map(p => (p._1, 1.0)).toSeq + } + else { + termCounts.filter(_._2 >= effectiveMinTF).toSeq + } + Vectors.sparse(dictBr.value.size, effectiveCounts) } dataset.withColumn($(outputCol), vectorizer(col($(inputCol)))) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala index 9c99990173..04f165c5f1 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala @@ -157,7 +157,7 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext (3, split("e e e e e"), Vectors.sparse(4, Seq()))) ).toDF("id", "words", "expected") - // minTF: count + // minTF: set frequency val cv = new CountVectorizerModel(Array("a", "b", "c", "d")) .setInputCol("words") .setOutputCol("features") @@ -168,6 +168,23 @@ class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext } } + test("CountVectorizerModel with binary") { + val df = sqlContext.createDataFrame(Seq( + (0, split("a a a b b c"), Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0)))), + (1, split("c c c"), Vectors.sparse(4, Seq((2, 1.0)))), + (2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))) + )).toDF("id", "words", "expected") + + val cv = new CountVectorizerModel(Array("a", "b", "c", "d")) + .setInputCol("words") + .setOutputCol("features") + .setBinary(true) + cv.transform(df).select("features", "expected").collect().foreach { + case Row(features: Vector, expected: Vector) => + assert(features ~== expected absTol 1e-14) + } + } + test("CountVectorizer read/write") { val t = new CountVectorizer() .setInputCol("myInputCol") |