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
  • 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
  • DataValidators
  • KMeansDataGenerator
  • LinearDataGenerator
  • Loader
  • LogisticRegressionDataGenerator
  • MFDataGenerator
  • MLUtils
  • SVMDataGenerator
  • Saveable

package util

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. Protected

Type Members

  1. trait Loader[M <: Saveable] extends AnyRef

    Trait for classes which can load models and transformers from files.

    Trait for classes which can load models and transformers from files. This should be inherited by an object paired with the model class.

    Annotations
    @Since("1.3.0")
  2. trait Saveable extends AnyRef

    Trait for models and transformers which may be saved as files.

    Trait for models and transformers which may be saved as files. This should be inherited by the class which implements model instances.

    Annotations
    @Since("1.3.0")

Value Members

  1. object DataValidators extends Logging

    A collection of methods used to validate data before applying ML algorithms.

    A collection of methods used to validate data before applying ML algorithms.

    Annotations
    @Since("0.8.0")
  2. object KMeansDataGenerator

    Generate test data for KMeans.

    Generate test data for KMeans. This class first chooses k cluster centers from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian cluster with scale 1 around each center.

    Annotations
    @Since("0.8.0")
  3. object LinearDataGenerator

    Generate sample data used for Linear Data.

    Generate sample data used for Linear Data. This class generates uniformly random values for every feature and adds Gaussian noise with mean eps to the response variable Y.

    Annotations
    @Since("0.8.0")
  4. object LogisticRegressionDataGenerator

    Generate test data for LogisticRegression.

    Generate test data for LogisticRegression. This class chooses positive labels with probability probOne and scales features for positive examples by eps.

    Annotations
    @Since("0.8.0")
  5. object MFDataGenerator

    Generate RDD(s) containing data for Matrix Factorization.

    Generate RDD(s) containing data for Matrix Factorization.

    This method samples training entries according to the oversampling factor 'trainSampFact', which is a multiplicative factor of the number of degrees of freedom of the matrix: rank*(m+n-rank).

    It optionally samples entries for a testing matrix using 'testSampFact', the percentage of the number of training entries to use for testing.

    This method takes the following inputs: sparkMaster (String) The master URL. outputPath (String) Directory to save output. m (Int) Number of rows in data matrix. n (Int) Number of columns in data matrix. rank (Int) Underlying rank of data matrix. trainSampFact (Double) Oversampling factor. noise (Boolean) Whether to add gaussian noise to training data. sigma (Double) Standard deviation of added gaussian noise. test (Boolean) Whether to create testing RDD. testSampFact (Double) Percentage of training data to use as test data.

    Annotations
    @Since("0.8.0")
  6. object MLUtils extends Logging

    Helper methods to load, save and pre-process data used in MLLib.

    Helper methods to load, save and pre-process data used in MLLib.

    Annotations
    @Since("0.8.0")
  7. object SVMDataGenerator

    Generate sample data used for SVM.

    Generate sample data used for SVM. This class generates uniform random values for the features and adds Gaussian noise with weight 0.1 to generate labels.

    Annotations
    @Since("0.8.0")

Ungrouped