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  • package root
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    root
  • package org
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    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 ChiSqSelector extends Serializable

Creates a ChiSquared feature selector. The selector supports different selection methods: numTopFeatures, percentile, fpr, fdr, fwe.

  • numTopFeatures chooses a fixed number of top features according to a chi-squared test.
  • percentile is similar but chooses a fraction of all features instead of a fixed number.
  • fpr chooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.
  • fdr uses the [Benjamini-Hochberg procedure] (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure) to choose all features whose false discovery rate is below a threshold.
  • fwe chooses all features whose p-values are below a threshold. The threshold is scaled by 1/numFeatures, thus controlling the family-wise error rate of selection. By default, the selection method is numTopFeatures, with the default number of top features set to 50.
Annotations
@Since("1.3.0")
Source
ChiSqSelector.scala
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Instance Constructors

  1. new ChiSqSelector(numTopFeatures: Int)

    The is the same to call this() and setNumTopFeatures(numTopFeatures)

    The is the same to call this() and setNumTopFeatures(numTopFeatures)

    Annotations
    @Since("1.3.0")
  2. new ChiSqSelector()
    Annotations
    @Since("2.1.0")

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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: AnyRef): Boolean
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  8. var fdr: Double
  9. def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel

    Returns a ChiSquared feature selector.

    Returns a ChiSquared feature selector.

    data

    an RDD[LabeledPoint] containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value. Apply feature discretizer before using this function.

    Annotations
    @Since("1.3.0")
  10. var fpr: Double
  11. var fwe: Double
  12. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  13. def hashCode(): Int
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  14. final def isInstanceOf[T0]: Boolean
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  15. final def ne(arg0: AnyRef): Boolean
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  16. final def notify(): Unit
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  17. final def notifyAll(): Unit
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  18. var numTopFeatures: Int
  19. var percentile: Double
  20. var selectorType: String
  21. def setFdr(value: Double): ChiSqSelector.this.type
    Annotations
    @Since("2.2.0")
  22. def setFpr(value: Double): ChiSqSelector.this.type
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    @Since("2.1.0")
  23. def setFwe(value: Double): ChiSqSelector.this.type
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    @Since("2.2.0")
  24. def setNumTopFeatures(value: Int): ChiSqSelector.this.type
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    @Since("1.6.0")
  25. def setPercentile(value: Double): ChiSqSelector.this.type
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    @Since("2.1.0")
  26. def setSelectorType(value: String): ChiSqSelector.this.type
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    @Since("2.1.0")
  27. final def synchronized[T0](arg0: => T0): T0
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  28. def toString(): String
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  29. final def wait(arg0: Long, arg1: Int): Unit
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  30. final def wait(arg0: Long): Unit
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    Deprecated

    (Since version 9)

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