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author | RJ Nowling <rnowling@gmail.com> | 2014-09-26 09:58:47 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-09-26 09:58:47 -0700 |
commit | ec9df6a765701fa41390083df12e1dc1fee50662 (patch) | |
tree | f0c7743aa58693c8563dd9dee1ead54e36689436 /mllib/src/main | |
parent | d16e161d744b27291fd2ee7e3578917ee14d83f9 (diff) | |
download | spark-ec9df6a765701fa41390083df12e1dc1fee50662.tar.gz spark-ec9df6a765701fa41390083df12e1dc1fee50662.tar.bz2 spark-ec9df6a765701fa41390083df12e1dc1fee50662.zip |
[SPARK-3614][MLLIB] Add minimumOccurence filtering to IDF
This PR for [SPARK-3614](https://issues.apache.org/jira/browse/SPARK-3614) adds functionality for filtering out terms which do not appear in at least a minimum number of documents.
This is implemented using a minimumOccurence parameter (default 0). When terms' document frequencies are less than minimumOccurence, their IDFs are set to 0, just like when the DF is 0. As a result, the TF-IDFs for the terms are found to be 0, as if the terms were not present in the documents.
This PR makes the following changes:
* Add a minimumOccurence parameter to the IDF and DocumentFrequencyAggregator classes.
* Create a parameter-less constructor for IDF with a default minimumOccurence value of 0 to remain backwards-compatibility with the original IDF API.
* Sets the IDFs to 0 for terms which DFs are less than minimumOccurence
* Add tests to the Spark IDFSuite and Java JavaTfIdfSuite test suites
* Updated the MLLib Feature Extraction programming guide to describe the new feature
Author: RJ Nowling <rnowling@gmail.com>
Closes #2494 from rnowling/spark-3614-idf-filter and squashes the following commits:
0aa3c63 [RJ Nowling] Fix identation
e6523a8 [RJ Nowling] Remove unnecessary toDouble's from IDFSuite
bfa82ec [RJ Nowling] Add space after if
30d20b3 [RJ Nowling] Add spaces around equals signs
9013447 [RJ Nowling] Add space before division operator
79978fc [RJ Nowling] Remove unnecessary semi-colon
40fd70c [RJ Nowling] Change minimumOccurence to minDocFreq in code and docs
47850ab [RJ Nowling] Changed minimumOccurence to Int from Long
9fb4093 [RJ Nowling] Remove unnecessary lines from IDF class docs
1fc09d8 [RJ Nowling] Add backwards-compatible constructor to DocumentFrequencyAggregator
1801fd2 [RJ Nowling] Fix style errors in IDF.scala
6897252 [RJ Nowling] Preface minimumOccurence members with val to make them final and immutable
a200bab [RJ Nowling] Remove unnecessary else statement
4b974f5 [RJ Nowling] Remove accidentally-added import from testing
c0cc643 [RJ Nowling] Add minimumOccurence filtering to IDF
Diffstat (limited to 'mllib/src/main')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala | 37 |
1 files changed, 33 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala index d40d5553c1..720bb70b08 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala @@ -30,9 +30,18 @@ import org.apache.spark.rdd.RDD * Inverse document frequency (IDF). * The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`, where `m` is the total * number of documents and `d(t)` is the number of documents that contain term `t`. + * + * This implementation supports filtering out terms which do not appear in a minimum number + * of documents (controlled by the variable `minDocFreq`). For terms that are not in + * at least `minDocFreq` documents, the IDF is found as 0, resulting in TF-IDFs of 0. + * + * @param minDocFreq minimum of documents in which a term + * should appear for filtering */ @Experimental -class IDF { +class IDF(val minDocFreq: Int) { + + def this() = this(0) // TODO: Allow different IDF formulations. @@ -41,7 +50,8 @@ class IDF { * @param dataset an RDD of term frequency vectors */ def fit(dataset: RDD[Vector]): IDFModel = { - val idf = dataset.treeAggregate(new IDF.DocumentFrequencyAggregator)( + val idf = dataset.treeAggregate(new IDF.DocumentFrequencyAggregator( + minDocFreq = minDocFreq))( seqOp = (df, v) => df.add(v), combOp = (df1, df2) => df1.merge(df2) ).idf() @@ -60,13 +70,16 @@ class IDF { private object IDF { /** Document frequency aggregator. */ - class DocumentFrequencyAggregator extends Serializable { + class DocumentFrequencyAggregator(val minDocFreq: Int) extends Serializable { /** number of documents */ private var m = 0L /** document frequency vector */ private var df: BDV[Long] = _ + + def this() = this(0) + /** Adds a new document. */ def add(doc: Vector): this.type = { if (isEmpty) { @@ -123,7 +136,18 @@ private object IDF { val inv = new Array[Double](n) var j = 0 while (j < n) { - inv(j) = math.log((m + 1.0)/ (df(j) + 1.0)) + /* + * If the term is not present in the minimum + * number of documents, set IDF to 0. This + * will cause multiplication in IDFModel to + * set TF-IDF to 0. + * + * Since arrays are initialized to 0 by default, + * we just omit changing those entries. + */ + if(df(j) >= minDocFreq) { + inv(j) = math.log((m + 1.0) / (df(j) + 1.0)) + } j += 1 } Vectors.dense(inv) @@ -140,6 +164,11 @@ class IDFModel private[mllib] (val idf: Vector) extends Serializable { /** * Transforms term frequency (TF) vectors to TF-IDF vectors. + * + * If `minDocFreq` was set for the IDF calculation, + * the terms which occur in fewer than `minDocFreq` + * documents will have an entry of 0. + * * @param dataset an RDD of term frequency vectors * @return an RDD of TF-IDF vectors */ |