Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package feature
    Definition Classes
    mllib
  • ChiSqSelector
  • ChiSqSelectorModel
  • ElementwiseProduct
  • HashingTF
  • IDF
  • IDFModel
  • Normalizer
  • PCA
  • PCAModel
  • StandardScaler
  • StandardScalerModel
  • VectorTransformer
  • Word2Vec
  • Word2VecModel

class IDF extends AnyRef

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. The document frequency is 0 as well for such terms

Annotations
@Since( "1.1.0" )
Source
IDF.scala
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AnyRef, Any
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Instance Constructors

  1. new IDF()
    Annotations
    @Since( "1.1.0" )
  2. new IDF(minDocFreq: Int)

    minDocFreq

    minimum of documents in which a term should appear for filtering

    Annotations
    @Since( "1.2.0" )

Value Members

  1. def fit(dataset: JavaRDD[Vector]): IDFModel

    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    a JavaRDD of term frequency vectors

    Annotations
    @Since( "1.1.0" )
  2. def fit(dataset: RDD[Vector]): IDFModel

    Computes the inverse document frequency.

    Computes the inverse document frequency.

    dataset

    an RDD of term frequency vectors

    Annotations
    @Since( "1.1.0" )
  3. val minDocFreq: Int
    Annotations
    @Since( "1.2.0" )