org.apache.spark.ml.feature.IDF Scala Examples

The following examples show how to use org.apache.spark.ml.feature.IDF. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Example 1
Source File: TfIdfExample.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.sql.SparkSession

object TfIdfExample {

  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("TfIdfExample")
      .getOrCreate()

    // $example on$
    val sentenceData = spark.createDataFrame(Seq(
      (0.0, "Hi I heard about Spark"),
      (0.0, "I wish Java could use case classes"),
      (1.0, "Logistic regression models are neat")
    )).toDF("label", "sentence")

    val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
    val wordsData = tokenizer.transform(sentenceData)

    val hashingTF = new HashingTF()
      .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)

    val featurizedData = hashingTF.transform(wordsData)
    // alternatively, CountVectorizer can also be used to get term frequency vectors

    val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
    val idfModel = idf.fit(featurizedData)

    val rescaledData = idfModel.transform(featurizedData)
    rescaledData.select("label", "features").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
Example 2
Source File: TokenizerSuite.scala    From spark-nkp   with Apache License 2.0 5 votes vote down vote up 
package com.github.uosdmlab.nkp

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{CountVectorizer, IDF}
import org.apache.spark.sql.SparkSession
import org.scalatest.{BeforeAndAfterAll, BeforeAndAfter, FunSuite}


class TokenizerSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfter {

  private var tokenizer: Tokenizer = _

  private val spark: SparkSession =
    SparkSession.builder()
      .master("local[2]")
      .appName("Tokenizer Suite")
      .getOrCreate

  spark.sparkContext.setLogLevel("WARN")

  import spark.implicits._

  override protected def afterAll(): Unit = {
    try {
      spark.stop
    } finally {
      super.afterAll()
    }
  }

  before {
    tokenizer = new Tokenizer()
      .setInputCol("text")
      .setOutputCol("words")
  }

  private val df = spark.createDataset(
    Seq(
      "아버지가방에들어가신다.",
      "사랑해요 제플린!",
      "스파크는 재밌어",
      "나는야 데이터과학자",
      "데이터야~ 놀자~"
    )
  ).toDF("text")

  test("Default parameters") {
    assert(tokenizer.getFilter sameElements Array.empty[String])
  }

  test("Basic operation") {
    val words = tokenizer.transform(df)

    assert(df.count == words.count)
    assert(words.schema.fieldNames.contains(tokenizer.getOutputCol))
  }

  test("POS filter") {
    val nvTokenizer = new Tokenizer()
      .setInputCol("text")
      .setOutputCol("nvWords")
      .setFilter("N", "V")

    val words = tokenizer.transform(df).join(nvTokenizer.transform(df), "text")

    assert(df.count == words.count)
    assert(words.schema.fieldNames.contains(nvTokenizer.getOutputCol))
    assert(words.where(s"SIZE(${tokenizer.getOutputCol}) < SIZE(${nvTokenizer.getOutputCol})").count == 0)
  }

  test("TF-IDF pipeline") {
    tokenizer.setFilter("N")

    val cntVec = new CountVectorizer()
      .setInputCol("words")
      .setOutputCol("tf")

    val idf = new IDF()
      .setInputCol("tf")
      .setOutputCol("tfidf")

    val pipe = new Pipeline()
      .setStages(Array(tokenizer, cntVec, idf))

    val pipeModel = pipe.fit(df)

    val result = pipeModel.transform(df)

    assert(result.count == df.count)

    val fields = result.schema.fieldNames
    assert(fields.contains(tokenizer.getOutputCol))
    assert(fields.contains(cntVec.getOutputCol))
    assert(fields.contains(idf.getOutputCol))

    result.show
  }
}
Example 3
Source File: IDFWrapper.scala    From automl   with Apache License 2.0 5 votes vote down vote up 
package com.tencent.angel.spark.automl.feature.preprocess

import com.tencent.angel.spark.automl.feature.InToOutRelation.{InToOutRelation, OneToOne}
import com.tencent.angel.spark.automl.feature.TransformerWrapper
import org.apache.spark.ml.feature.IDF

class IDFWrapper extends TransformerWrapper {

  override val transformer = new IDF()
  override var parent: TransformerWrapper = _

  override val hasMultiInputs: Boolean = false
  override val hasMultiOutputs: Boolean = false
  override val needAncestorInputs: Boolean = false

  override val relation: InToOutRelation = OneToOne

  override val requiredInputCols: Array[String] = Array("rawFeatures")
  override val requiredOutputCols: Array[String] = Array("outIDF")

  override def declareInAndOut(): this.type = {
    transformer.asInstanceOf[IDF].setInputCol(getInputCols(0))
    transformer.asInstanceOf[IDF].setOutputCol(getOutputCols(0))
    this
  }
}
Example 4
Source File: TfIdfExample.scala    From sparkoscope   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.sql.SparkSession

object TfIdfExample {

  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("TfIdfExample")
      .getOrCreate()

    // $example on$
    val sentenceData = spark.createDataFrame(Seq(
      (0.0, "Hi I heard about Spark"),
      (0.0, "I wish Java could use case classes"),
      (1.0, "Logistic regression models are neat")
    )).toDF("label", "sentence")

    val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
    val wordsData = tokenizer.transform(sentenceData)

    val hashingTF = new HashingTF()
      .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)

    val featurizedData = hashingTF.transform(wordsData)
    // alternatively, CountVectorizer can also be used to get term frequency vectors

    val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
    val idfModel = idf.fit(featurizedData)

    val rescaledData = idfModel.transform(featurizedData)
    rescaledData.select("label", "features").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
Example 5
Source File: Preprocessor.scala    From CkoocNLP   with Apache License 2.0 5 votes vote down vote up 
package functions

import config.paramconf.PreprocessParams
import functions.clean.Cleaner
import functions.segment.Segmenter
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{CountVectorizer, IDF, StopWordsRemover, StringIndexer}
import org.apache.spark.sql.DataFrame


  def preprocess(data: DataFrame): Pipeline = {
    val spark = data.sparkSession
    val params = new PreprocessParams

    val indexModel = new StringIndexer()
      .setHandleInvalid(params.handleInvalid)
      .setInputCol("label")
      .setOutputCol("indexedLabel")
      .fit(data)

    val cleaner = new Cleaner()
      .setFanJian(params.fanjian)
      .setQuanBan(params.quanban)
      .setMinLineLen(params.minLineLen)
      .setInputCol("content")
      .setOutputCol("cleand")

    val segmenter = new Segmenter()
      .isAddNature(params.addNature)
      .isDelEn(params.delEn)
      .isDelNum(params.delNum)
      .isNatureFilter(params.natureFilter)
      .setMinTermLen(params.minTermLen)
      .setMinTermNum(params.minTermNum)
      .setSegType(params.segmentType)
      .setInputCol(cleaner.getOutputCol)
      .setOutputCol("segmented")

    val stopwords = spark.sparkContext.textFile(params.stopwordFilePath).collect()
    val remover = new StopWordsRemover()
      .setStopWords(stopwords)
      .setInputCol(segmenter.getOutputCol)
      .setOutputCol("removed")

    val vectorizer = new CountVectorizer()
      .setMinTF(params.minTF)
      .setVocabSize(params.vocabSize)
      .setInputCol(remover.getOutputCol)
      .setOutputCol("vectorized")

    val idf = new IDF()
      .setMinDocFreq(params.minDocFreq)
      .setInputCol(vectorizer.getOutputCol)
      .setOutputCol("features")

    val stages = Array(cleaner, indexModel, segmenter, remover, vectorizer, idf)
    new Pipeline().setStages(stages)
  }
}
Example 6
Source File: TimerSuite.scala    From mmlspark   with MIT License 5 votes vote down vote up 
// Copyright (C) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License. See LICENSE in project root for information.

package com.microsoft.ml.spark.stages

import com.microsoft.ml.spark.core.test.fuzzing.{EstimatorFuzzing, TestObject}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
import org.apache.spark.ml.util.MLReadable
import org.apache.spark.sql.DataFrame

class TimerSuite extends EstimatorFuzzing[Timer] {

  lazy val df: DataFrame = session
    .createDataFrame(Seq((0, "Hi I"),
                         (1, "I wish for snow today"),
                         (2, "we Cant go to the park, because of the snow!"),
                         (3, "")))
    .toDF("label", "sentence")

  test("Work with transformers and estimators") {

    val tok = new Tokenizer()
      .setInputCol("sentence")
      .setOutputCol("tokens")

    val df2 = new Timer().setStage(tok).fit(df).transform(df)

    val df3 = new HashingTF().setInputCol("tokens").setOutputCol("hash").transform(df2)

    val idf = new IDF().setInputCol("hash").setOutputCol("idf")

    val df4 = new Timer().setStage(idf).fit(df3).transform(df3)

  }

  test("should work within pipelines") {
    val tok = new Tokenizer()
      .setInputCol("sentence")
      .setOutputCol("tokens")
    val ttok = new Timer().setStage(tok)
    val hash = new HashingTF().setInputCol("tokens").setOutputCol("hash")
    val idf  = new IDF().setInputCol("hash").setOutputCol("idf")
    val tidf = new Timer().setStage(idf)
    val pipe = new Pipeline().setStages(Array(ttok, hash, tidf))
    pipe.fit(df).transform(df)
  }

  test("should be able to turn off timing") {
    val tok = new Tokenizer()
      .setInputCol("sentence")
      .setOutputCol("tokens")
    val ttok = new Timer().setStage(tok)
    val hash = new HashingTF().setInputCol("tokens").setOutputCol("hash")
    val idf  = new IDF().setInputCol("hash").setOutputCol("idf")
    val tidf = new Timer().setStage(idf)
    val pipe = new Pipeline().setStages(Array(ttok, hash, tidf))
    val model = pipe.fit(df)

    println("Transforming")
    println(model.stages(0).params.foreach(println(_)))
    model.stages(0).asInstanceOf[TimerModel].setDisable(true)
    model.stages(2).asInstanceOf[TimerModel].setDisable(true)

    println("here")
    println(model.stages(0).getParam("disableMaterialization"))

    model.stages(0).params.foreach(p =>println("foo: " + p.toString))

    model.transform(df)
  }

  val reader: MLReadable[_] = Timer
  val modelReader: MLReadable[_] = TimerModel

  override def testObjects(): Seq[TestObject[Timer]] = Seq(new TestObject[Timer]({
    val tok = new Tokenizer()
      .setInputCol("sentence")
      .setOutputCol("tokens")
    new Timer().setStage(tok)
  }, df))
}
Example 7
Source File: TfIdfExample.scala    From multi-tenancy-spark   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.sql.SparkSession

object TfIdfExample {

  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("TfIdfExample")
      .getOrCreate()

    // $example on$
    val sentenceData = spark.createDataFrame(Seq(
      (0.0, "Hi I heard about Spark"),
      (0.0, "I wish Java could use case classes"),
      (1.0, "Logistic regression models are neat")
    )).toDF("label", "sentence")

    val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
    val wordsData = tokenizer.transform(sentenceData)

    val hashingTF = new HashingTF()
      .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)

    val featurizedData = hashingTF.transform(wordsData)
    // alternatively, CountVectorizer can also be used to get term frequency vectors

    val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
    val idfModel = idf.fit(featurizedData)

    val rescaledData = idfModel.transform(featurizedData)
    rescaledData.select("label", "features").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
Example 8
Source File: IDFTest.scala    From TransmogrifAI   with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
package com.salesforce.op.stages.impl.feature

import com.salesforce.op._
import com.salesforce.op.features.types._
import com.salesforce.op.test.{TestFeatureBuilder, TestSparkContext}
import com.salesforce.op.utils.spark.RichDataset._
import org.apache.spark.ml.feature.IDF
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.{Estimator, Transformer}
import org.junit.runner.RunWith
import org.scalatest.junit.JUnitRunner
import org.scalatest.{Assertions, FlatSpec, Matchers}


@RunWith(classOf[JUnitRunner])
class IDFTest extends FlatSpec with TestSparkContext {

  val data = Seq(
    Vectors.sparse(4, Array(1, 3), Array(1.0, 2.0)),
    Vectors.dense(0.0, 1.0, 2.0, 3.0),
    Vectors.sparse(4, Array(1), Array(1.0))
  )

  lazy val (ds, f1) = TestFeatureBuilder(data.map(_.toOPVector))

  Spec[IDF] should "compute inverted document frequency" in {
    val idf = f1.idf()
    val model = idf.originStage.asInstanceOf[Estimator[_]].fit(ds)
    val transformedData = model.asInstanceOf[Transformer].transform(ds)
    val results = transformedData.select(idf.name).collect(idf)

    idf.name shouldBe idf.originStage.getOutputFeatureName

    val expectedIdf = Vectors.dense(Array(0, 3, 1, 2).map { x =>
      math.log((data.length + 1.0) / (x + 1.0))
    })
    val expected = scaleDataWithIDF(data, expectedIdf)

    for {
      (res, exp) <- results.zip(expected)
      (x, y) <- res.value.toArray.zip(exp.toArray)
    } assert(math.abs(x - y) <= 1e-5)
  }

  it should "compute inverted document frequency when minDocFreq is 1" in {
    val idf = f1.idf(minDocFreq = 1)
    val model = idf.originStage.asInstanceOf[Estimator[_]].fit(ds)
    val transformedData = model.asInstanceOf[Transformer].transform(ds)
    val results = transformedData.select(idf.name).collect(idf)
    idf.name shouldBe idf.originStage.getOutputFeatureName

    val expectedIdf = Vectors.dense(Array(0, 3, 1, 2).map { x =>
      if (x > 0) math.log((data.length + 1.0) / (x + 1.0)) else 0
    })
    val expected = scaleDataWithIDF(data, expectedIdf)

    for {
      (res, exp) <- results.zip(expected)
      (x, y) <- res.value.toArray.zip(exp.toArray)
    } assert(math.abs(x - y) <= 1e-5)
  }

  private def scaleDataWithIDF(dataSet: Seq[Vector], model: Vector): Seq[Vector] = {
    dataSet.map {
      case data: DenseVector =>
        val res = data.toArray.zip(model.toArray).map { case (x, y) => x * y }
        Vectors.dense(res)
      case data: SparseVector =>
        val res = data.indices.zip(data.values).map { case (id, value) =>
          (id, value * model(id))
        }
        Vectors.sparse(data.size, res)
    }
  }

}
Example 9
Source File: TfIdfExample.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.{SQLContext, DataFrame}

    rescaledData.show()
    rescaledData.select("features", "label").take(3).foreach(println)
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println
Example 10
Source File: TfIdfExample.scala    From Spark-2.3.1   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.sql.SparkSession

object TfIdfExample {

  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("TfIdfExample")
      .getOrCreate()

    // $example on$
    val sentenceData = spark.createDataFrame(Seq(
      (0.0, "Hi I heard about Spark"),
      (0.0, "I wish Java could use case classes"),
      (1.0, "Logistic regression models are neat")
    )).toDF("label", "sentence")

    val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
    val wordsData = tokenizer.transform(sentenceData)

    val hashingTF = new HashingTF()
      .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)

    val featurizedData = hashingTF.transform(wordsData)
    // alternatively, CountVectorizer can also be used to get term frequency vectors

    val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
    val idfModel = idf.fit(featurizedData)

    val rescaledData = idfModel.transform(featurizedData)
    rescaledData.select("label", "features").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println
Example 11
Source File: TfIdfExample.scala    From BigDatalog   with Apache License 2.0 5 votes vote down vote up 
// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
// $example off$
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object TfIdfExample {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("TfIdfExample")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    // $example on$
    val sentenceData = sqlContext.createDataFrame(Seq(
      (0, "Hi I heard about Spark"),
      (0, "I wish Java could use case classes"),
      (1, "Logistic regression models are neat")
    )).toDF("label", "sentence")

    val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
    val wordsData = tokenizer.transform(sentenceData)
    val hashingTF = new HashingTF()
      .setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)
    val featurizedData = hashingTF.transform(wordsData)
    val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
    val idfModel = idf.fit(featurizedData)
    val rescaledData = idfModel.transform(featurizedData)
    rescaledData.select("features", "label").take(3).foreach(println)
    // $example off$
  }
}
// scalastyle:on println