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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 clustering

Definition Classes
mllib
BisectingKMeans
BisectingKMeansModel
DistanceMeasure
DistributedLDAModel
EMLDAOptimizer
GaussianMixture
GaussianMixtureModel
KMeans
KMeansModel
LDA
LDAModel
LDAOptimizer
LocalLDAModel
OnlineLDAOptimizer
PowerIterationClustering
PowerIterationClusteringModel
StreamingKMeans
StreamingKMeansModel

org.apache.spark.mllib.clustering

EMLDAOptimizer

final class EMLDAOptimizer extends LDAOptimizer

Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.

Currently, the underlying implementation uses Expectation-Maximization (EM), implemented according to the Asuncion et al. (2009) paper referenced below.

References:

Original LDA paper (journal version): Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
- This class implements their "smoothed" LDA model.
Paper which clearly explains several algorithms, including EM: Asuncion, Welling, Smyth, and Teh. "On Smoothing and Inference for Topic Models." UAI, 2009.

Annotations: @Since( "1.4.0" )
Source: LDAOptimizer.scala

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LDAOptimizer, AnyRef, Any

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def getKeepLastCheckpoint: Boolean
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).

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def setKeepLastCheckpoint(keepLastCheckpoint: Boolean): EMLDAOptimizer.this.type
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up).
If using checkpointing, this indicates whether to keep the last checkpoint (vs clean up). Deleting the checkpoint can cause failures if a data partition is lost, so set this bit with care.
Default: true

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@Since( "2.0.0" )
Note
Checkpoints will be cleaned up via reference counting, regardless.
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EMLDAOptimizer

final class EMLDAOptimizer extends LDAOptimizer

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Inherited from LDAOptimizer

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EMLDAOptimizer 

final class EMLDAOptimizer extends LDAOptimizer

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EMLDAOptimizer