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 Word2Vec extends Serializable with Logging

Word2Vec creates vector representation of words in a text corpus. The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.

We used skip-gram model in our implementation and hierarchical softmax method to train the model. The variable names in the implementation matches the original C implementation.

For original C implementation, see https://code.google.com/p/word2vec/ For research papers, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

Annotations
@Since("1.1.0")
Source
Word2Vec.scala
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Logging, Serializable, AnyRef, Any
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  1. Word2Vec
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Instance Constructors

  1. new Word2Vec()

Type Members

  1. implicit class LogStringContext extends AnyRef
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Value Members

  1. final def !=(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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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. def fit[S <: Iterable[String]](dataset: JavaRDD[S]): Word2VecModel

    Computes the vector representation of each word in vocabulary (Java version).

    Computes the vector representation of each word in vocabulary (Java version).

    dataset

    a JavaRDD of words

    returns

    a Word2VecModel

    Annotations
    @Since("1.1.0")
  9. def fit[S <: Iterable[String]](dataset: RDD[S]): Word2VecModel

    Computes the vector representation of each word in vocabulary.

    Computes the vector representation of each word in vocabulary.

    dataset

    an RDD of sentences, each sentence is expressed as an iterable collection of words

    returns

    a Word2VecModel

    Annotations
    @Since("1.1.0")
  10. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  11. def hashCode(): Int
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  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. final def isInstanceOf[T0]: Boolean
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  15. def isTraceEnabled(): Boolean
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  16. def log: Logger
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  17. def logDebug(msg: => String, throwable: Throwable): Unit
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  18. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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  19. def logDebug(entry: LogEntry): Unit
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  20. def logDebug(msg: => String): Unit
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  21. def logError(msg: => String, throwable: Throwable): Unit
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  22. def logError(entry: LogEntry, throwable: Throwable): Unit
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  23. def logError(entry: LogEntry): Unit
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  24. def logError(msg: => String): Unit
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  25. def logInfo(msg: => String, throwable: Throwable): Unit
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  26. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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  27. def logInfo(entry: LogEntry): Unit
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  28. def logInfo(msg: => String): Unit
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  29. def logName: String
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  30. def logTrace(msg: => String, throwable: Throwable): Unit
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  31. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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  32. def logTrace(entry: LogEntry): Unit
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  33. def logTrace(msg: => String): Unit
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  34. def logWarning(msg: => String, throwable: Throwable): Unit
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  35. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  36. def logWarning(entry: LogEntry): Unit
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  37. def logWarning(msg: => String): Unit
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  38. final def ne(arg0: AnyRef): Boolean
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  39. final def notify(): Unit
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    @IntrinsicCandidate() @native()
  40. final def notifyAll(): Unit
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    @IntrinsicCandidate() @native()
  41. def setLearningRate(learningRate: Double): Word2Vec.this.type

    Sets initial learning rate (default: 0.025).

    Sets initial learning rate (default: 0.025).

    Annotations
    @Since("1.1.0")
  42. def setMaxSentenceLength(maxSentenceLength: Int): Word2Vec.this.type

    Sets the maximum length (in words) of each sentence in the input data.

    Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to maxSentenceLength size (default: 1000)

    Annotations
    @Since("2.0.0")
  43. def setMinCount(minCount: Int): Word2Vec.this.type

    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).

    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).

    Annotations
    @Since("1.3.0")
  44. def setNumIterations(numIterations: Int): Word2Vec.this.type

    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

    Annotations
    @Since("1.1.0")
  45. def setNumPartitions(numPartitions: Int): Word2Vec.this.type

    Sets number of partitions (default: 1).

    Sets number of partitions (default: 1). Use a small number for accuracy.

    Annotations
    @Since("1.1.0")
  46. def setSeed(seed: Long): Word2Vec.this.type

    Sets random seed (default: a random long integer).

    Sets random seed (default: a random long integer).

    Annotations
    @Since("1.1.0")
  47. def setVectorSize(vectorSize: Int): Word2Vec.this.type

    Sets vector size (default: 100).

    Sets vector size (default: 100).

    Annotations
    @Since("1.1.0")
  48. def setWindowSize(window: Int): Word2Vec.this.type

    Sets the window of words (default: 5)

    Sets the window of words (default: 5)

    Annotations
    @Since("1.6.0")
  49. final def synchronized[T0](arg0: => T0): T0
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  50. def toString(): String
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  51. final def wait(arg0: Long, arg1: Int): Unit
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  52. final def wait(arg0: Long): Unit
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  53. final def wait(): Unit
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  54. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
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  1. def finalize(): Unit
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    Deprecated

    (Since version 9)

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