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 classification
    Definition Classes
    mllib
  • package clustering
    Definition Classes
    mllib
  • package evaluation
    Definition Classes
    mllib
  • package feature
    Definition Classes
    mllib
  • package fpm
    Definition Classes
    mllib
  • AssociationRules
  • FPGrowth
  • FPGrowthModel
  • PrefixSpan
  • PrefixSpanModel
  • package linalg
    Definition Classes
    mllib
  • package optimization
    Definition Classes
    mllib
  • package pmml
    Definition Classes
    mllib
  • package random
    Definition Classes
    mllib
  • package rdd
    Definition Classes
    mllib
  • package recommendation
    Definition Classes
    mllib
  • package regression
    Definition Classes
    mllib
  • package stat
    Definition Classes
    mllib
  • package tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package util
    Definition Classes
    mllib

package fpm

Ordering
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Visibility
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Type Members

  1. class AssociationRules extends Logging with Serializable

    Generates association rules from a RDD[FreqItemset[Item]].

    Generates association rules from a RDD[FreqItemset[Item]]. This method only generates association rules which have a single item as the consequent.

    Annotations
    @Since( "1.5.0" )
  2. class FPGrowth extends Logging with Serializable

    A parallel FP-growth algorithm to mine frequent itemsets.

    A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation. PFP distributes computation in such a way that each worker executes an independent group of mining tasks. The FP-Growth algorithm is described in Han et al., Mining frequent patterns without candidate generation.

    Annotations
    @Since( "1.3.0" )
    See also

    Association rule learning (Wikipedia)

  3. class FPGrowthModel[Item] extends Saveable with Serializable

    Model trained by FPGrowth, which holds frequent itemsets.

    Model trained by FPGrowth, which holds frequent itemsets.

    Item

    item type

    Annotations
    @Since( "1.3.0" )
  4. class PrefixSpan extends Logging with Serializable

    A parallel PrefixSpan algorithm to mine frequent sequential patterns.

    A parallel PrefixSpan algorithm to mine frequent sequential patterns. The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (see here).

    Annotations
    @Since( "1.5.0" )
    See also

    Sequential Pattern Mining (Wikipedia)

  5. class PrefixSpanModel[Item] extends Saveable with Serializable

    Model fitted by PrefixSpan

    Model fitted by PrefixSpan

    Item

    item type

    Annotations
    @Since( "1.5.0" )

Value Members

  1. object AssociationRules extends Serializable
    Annotations
    @Since( "1.5.0" )
  2. object FPGrowth extends Serializable
    Annotations
    @Since( "1.3.0" )
  3. object FPGrowthModel extends Loader[FPGrowthModel[_]] with Serializable
    Annotations
    @Since( "2.0.0" )
  4. object PrefixSpan extends Logging with Serializable
    Annotations
    @Since( "1.5.0" )
  5. object PrefixSpanModel extends Loader[PrefixSpanModel[_]] with Serializable
    Annotations
    @Since( "2.0.0" )

Ungrouped