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From feynmanliang <...@git.apache.org>
Subject [GitHub] spark pull request: [Spark-9028] [ML] Add CountVectorizer as an es...
Date Wed, 15 Jul 2015 22:32:57 GMT
Github user feynmanliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7388#discussion_r34737713
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
---
    @@ -19,45 +19,135 @@ package org.apache.spark.ml.feature
     import scala.collection.mutable
     
     import org.apache.spark.annotation.Experimental
    -import org.apache.spark.ml.UnaryTransformer
    -import org.apache.spark.ml.param.{ParamMap, ParamValidators, IntParam}
    -import org.apache.spark.ml.util.Identifiable
    -import org.apache.spark.mllib.linalg.{Vectors, VectorUDT, Vector}
    -import org.apache.spark.sql.types.{StringType, ArrayType, DataType}
    +import org.apache.spark.ml.param._
    +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
    +import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
    +import org.apache.spark.ml.{Estimator, Model}
    +import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.sql.functions._
    +import org.apache.spark.sql.types._
    +import org.apache.spark.sql.DataFrame
    +
    +/**
    + * Params for [[CountVectorizer]] and [[CountVectorizerModel]].
    + */
    +private[feature] trait CountVectorizerParams extends Params with HasInputCol with HasOutputCol
{
    +
    +  /**
    +   * size of the vocabulary.
    +   * If using Estimator, CountVectorizer will build a vocabulary that only consider the
top
    +   * vocabSize terms ordered by term frequency across the corpus.
    +   * Default: 10000
    +   * @group param
    +   */
    +  val vocabSize: IntParam = new IntParam(this, "vocabSize", "size of the vocabulary")
    +
    +  /** @group getParam */
    +  def getVocabSize: Int = $(vocabSize)
    +
    +  /** Validates and transforms the input schema. */
    +  protected def validateAndTransformSchema(schema: StructType): StructType = {
    +    SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true))
    +    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
    +  }
    +
    +  override def validateParams(): Unit = {
    +    require($(vocabSize) > 0, s"The vocabulary size (${$(vocabSize)}) must be above
0.")
    +  }
    +}
     
     /**
      * :: Experimental ::
    - * Converts a text document to a sparse vector of token counts.
    - * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
    + * Extracts a vocabulary from document collections and generates a CountVectorizerModel.
      */
    -@Experimental
    -class CountVectorizerModel (override val uid: String, val vocabulary: Array[String])
    -  extends UnaryTransformer[Seq[String], Vector, CountVectorizerModel] {
    +class CountVectorizer(override val uid: String)
    +  extends Estimator[CountVectorizerModel] with CountVectorizerParams {
     
    -  def this(vocabulary: Array[String]) =
    -    this(Identifiable.randomUID("cntVec"), vocabulary)
    +  def this() = this(Identifiable.randomUID("cntVec"))
     
       /**
    -   * Corpus-specific filter to ignore scarce words in a document. For each document,
terms with
    -   * frequency (count) less than the given threshold are ignored.
    +   * The minimum number of times a token must appear in the corpus to be included in
the vocabulary
        * Default: 1
        * @group param
        */
    -  val minTermFreq: IntParam = new IntParam(this, "minTermFreq",
    -    "minimum frequency (count) filter used to neglect scarce words (>= 1). For each
document, " +
    -      "terms with frequency less than the given threshold are ignored.", ParamValidators.gtEq(1))
    +  val minCount: IntParam = new IntParam(this, "minCount",
    +    "minimum number of times a token must appear in the corpus to be included in the
vocabulary."
    +    , ParamValidators.gtEq(1))
    +
    +  /** @group getParam */
    +  def getMinCount: Int = $(minCount)
     
       /** @group setParam */
    -  def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
    +  def setInputCol(value: String): this.type = set(inputCol, value)
     
    -  /** @group getParam */
    -  def getMinTermFreq: Int = $(minTermFreq)
    +  /** @group setParam */
    +  def setOutputCol(value: String): this.type = set(outputCol, value)
     
    -  setDefault(minTermFreq -> 1)
    +  /** @group setParam */
    +  def setVocabSize(value: Int): this.type = set(vocabSize, value)
    +
    +  /** @group setParam */
    +  def setMinCount(value: Int): this.type = set(minCount, value)
     
    -  override protected def createTransformFunc: Seq[String] => Vector = {
    +  setDefault(vocabSize -> 10000, minCount -> 1)
    +
    +  override def fit(dataset: DataFrame): CountVectorizerModel = {
    +    transformSchema(dataset.schema, logging = true)
    +    val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
    +    val min_count = $(minCount)
    +    val vocab_size = $(vocabSize)
    --- End diff --
    
    ditto to L98


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