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[Spark-8169] [ML] Add StopWordsRemover as a transformer #6742

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Original file line number Diff line number Diff line change
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.ml.feature

import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
import org.apache.spark.ml.param.{ParamMap, BooleanParam, Param}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.{StringType, StructField, ArrayType, StructType}
import org.apache.spark.sql.functions.{col, udf}

/**
* stop words list
*/
private object StopWords{
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space before {


/**
* Use the same default stopwords list as scikit-learn.
* The original list can be found from "Glasgow Information Retrieval Group"
* [[http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words]]
*/
val EnglishStopWords = Array( "a", "about", "above", "across", "after", "afterwards", "again",
"against", "all", "almost", "alone", "along", "already", "also", "although", "always",
"am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
"any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
"around", "as", "at", "back", "be", "became", "because", "become",
"becomes", "becoming", "been", "before", "beforehand", "behind", "being",
"below", "beside", "besides", "between", "beyond", "bill", "both",
"bottom", "but", "by", "call", "can", "cannot", "cant", "co", "con",
"could", "couldnt", "cry", "de", "describe", "detail", "do", "done",
"down", "due", "during", "each", "eg", "eight", "either", "eleven", "else",
"elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone",
"everything", "everywhere", "except", "few", "fifteen", "fify", "fill",
"find", "fire", "first", "five", "for", "former", "formerly", "forty",
"found", "four", "from", "front", "full", "further", "get", "give", "go",
"had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter",
"hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his",
"how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed",
"interest", "into", "is", "it", "its", "itself", "keep", "last", "latter",
"latterly", "least", "less", "ltd", "made", "many", "may", "me",
"meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly",
"move", "much", "must", "my", "myself", "name", "namely", "neither",
"never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone",
"nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on",
"once", "one", "only", "onto", "or", "other", "others", "otherwise", "our",
"ours", "ourselves", "out", "over", "own", "part", "per", "perhaps",
"please", "put", "rather", "re", "same", "see", "seem", "seemed",
"seeming", "seems", "serious", "several", "she", "should", "show", "side",
"since", "sincere", "six", "sixty", "so", "some", "somehow", "someone",
"something", "sometime", "sometimes", "somewhere", "still", "such",
"system", "take", "ten", "than", "that", "the", "their", "them",
"themselves", "then", "thence", "there", "thereafter", "thereby",
"therefore", "therein", "thereupon", "these", "they", "thick", "thin",
"third", "this", "those", "though", "three", "through", "throughout",
"thru", "thus", "to", "together", "too", "top", "toward", "towards",
"twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us",
"very", "via", "was", "we", "well", "were", "what", "whatever", "when",
"whence", "whenever", "where", "whereafter", "whereas", "whereby",
"wherein", "whereupon", "wherever", "whether", "which", "while", "whither",
"who", "whoever", "whole", "whom", "whose", "why", "will", "with",
"within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves")
}

/**
* :: Experimental ::
* A feature transformer that filters out stop words from input
* @see [[http://en.wikipedia.org/wiki/Stop_words]]
*/
@Experimental
class StopWordsRemover(override val uid: String)
extends Transformer with HasInputCol with HasOutputCol {

def this() = this(Identifiable.randomUID("stopWords"))

/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)

/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)

/**
* the stop words set to be filtered out
* @group param
*/
val stopWords: Param[Array[String]] = new Param(this, "stopWords", "stop words")

/** @group setParam */
def setStopWords(value: Array[String]): this.type = set(stopWords, value)

/** @group getParam */
def getStopWords: Array[String] = $(stopWords)

/**
* whether to do a case sensitive comparison over the stop words
* @group param
*/
val caseSensitive: BooleanParam = new BooleanParam(this, "caseSensitive",
"whether to do case-sensitive comparison during filtering")

/** @group setParam */
def setCaseSensitive(value: Boolean): this.type = set(caseSensitive, value)

/** @group getParam */
def getCaseSensitive: Boolean = $(caseSensitive)

setDefault(stopWords -> StopWords.EnglishStopWords, caseSensitive -> false)

override def transform(dataset: DataFrame): DataFrame = {
val outputSchema = transformSchema(dataset.schema)
val stopwordsSet = $(stopWords).toSet
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stopWordsSet

val lowerStopWords = stopwordsSet.map(_.toLowerCase)
val t = udf { terms: Seq[String] =>
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This will serialize both stopwordsSet and lowerStopWords into the UDF. We can do this:

val t = if ($(caseSensitive)) {
  udf { term: Seq[String] =>
    terms.filter(s => s != null || !stopwordsSet.contains(s))
  }
} else {
  udf { ...
  }
}

if ($(caseSensitive)) {
terms.filter(s => s == null || !stopwordsSet.contains(s))
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Do we want to keep null or not? What is the behavior in other NLP libraries? If we want to keep null, we should document it. I would suggest removing this check. If the stop words list contains null, we remove null. Otherwise, we keep null, just treating null as a normal word.

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Treating null as a normal word sounds great. Most other NLP libraries will assume that there's no null in the input array. I didn't add null to the default English set and will add note in doc about preserving null values by default. Let me know if you think we should just remove all nulls.

} else {
terms.filter(s => s == null || !lowerStopWords.contains(s.toLowerCase))
}
}
val metadata = outputSchema($(outputCol)).metadata
dataset.select(col("*"), t(col($(inputCol))).as($(outputCol), metadata))
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Why not dataset.withColumn($(outputCol), t(col($(inputCol))))?

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Actually NVM I think what your doing is better because it preserves metadata

}

override def transformSchema(schema: StructType): StructType = {
val inputType = schema($(inputCol)).dataType
require(inputType.sameType(ArrayType(StringType)),
s"Input type must be ArrayType(StringType) but got $inputType.")
val outputFields = schema.fields :+
StructField($(outputCol), inputType, schema($(inputCol)).nullable)
StructType(outputFields)
}

override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
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RegexTokenizer -> StopWordsRemover :)

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Sorry about it. Maybe we should add ut for copy...

}
Original file line number Diff line number Diff line change
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.ml.feature

import org.apache.spark.SparkFunSuite
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}

object StopWordsRemoverSuite extends SparkFunSuite {
def testStopWordsRemover(t: StopWordsRemover, dataset: DataFrame): Unit = {
t.transform(dataset)
.select("filtered", "expected")
.collect()
.foreach { case Row(tokens, wantedTokens) =>
assert(tokens === wantedTokens)
}
}
}

class StopWordsRemoverSuite extends SparkFunSuite with MLlibTestSparkContext {
import org.apache.spark.ml.feature.StopWordsRemoverSuite._
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For local import, it should be okay to write import StopWordsRemoverSuite._.


test("StopWordsRemover default") {
val remover = new StopWordsRemover()
.setInputCol("raw")
.setOutputCol("filtered")
val dataSet = sqlContext.createDataFrame(Seq(
(Seq("test", "test"), Seq("test", "test")),
(Seq("a", "b", "c", "d"), Seq("b", "c", "d")),
(Seq("a", "the", "an"), Seq()),
(Seq("A", "The", "AN"), Seq()),
(Seq(null), Seq(null)),
(Seq(), Seq())
)).toDF("raw", "expected")

testStopWordsRemover(remover, dataSet)
}

test("StopWordsRemover case sensitive") {
val remover = new StopWordsRemover()
.setInputCol("raw")
.setOutputCol("filtered")
.setCaseSensitive(true)
val dataSet = sqlContext.createDataFrame(Seq(
(Seq("A"), Seq("A")),
(Seq("The", "the"), Seq("The"))
)).toDF("raw", "expected")

testStopWordsRemover(remover, dataSet)
}

test("StopWordsRemover with additional words") {
val stopWords = StopWords.EnglishStopWords ++ Array("python", "scala")
val remover = new StopWordsRemover()
.setInputCol("raw")
.setOutputCol("filtered")
.setStopWords(stopWords)
val dataSet = sqlContext.createDataFrame(Seq(
(Seq("python", "scala", "a"), Seq()),
(Seq("Python", "Scala", "swift"), Seq("swift"))
)).toDF("raw", "expected")

testStopWordsRemover(remover, dataSet)
}
}