Packages

class Doc2VecApproach extends AnnotatorApproach[Doc2VecModel] with HasStorageRef

Trains a Word2Vec model that creates vector representations 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 use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a hierarchical softmax method to train the model. The variable names in the implementation match the original C implementation.

For instantiated/pretrained models, see Doc2VecModel.

Sources :

For the original C implementation, see https://code.google.com/p/word2vec/

For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.annotator.{Tokenizer, Doc2VecApproach}
import com.johnsnowlabs.nlp.base.DocumentAssembler
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols(Array("document"))
  .setOutputCol("token")

val embeddings = new Doc2VecApproach()
  .setInputCols("token")
  .setOutputCol("embeddings")

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    tokenizer,
    embeddings
  ))

val path = "src/test/resources/spell/sherlockholmes.txt"
val dataset = spark.sparkContext.textFile(path)
  .toDF("text")
val pipelineModel = pipeline.fit(dataset)
Linear Supertypes
HasStorageRef, ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[Doc2VecModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[Doc2VecModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. Doc2VecApproach
  2. HasStorageRef
  3. ParamsAndFeaturesWritable
  4. HasFeatures
  5. AnnotatorApproach
  6. CanBeLazy
  7. DefaultParamsWritable
  8. MLWritable
  9. HasOutputAnnotatorType
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new Doc2VecApproach()
  2. new Doc2VecApproach(uid: String)

Type Members

  1. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): Doc2VecModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    Doc2VecApproachAnnotatorApproach
  12. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  13. final def clear(param: Param[_]): Doc2VecApproach.this.type
    Definition Classes
    Params
  14. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  15. final def copy(extra: ParamMap): Estimator[Doc2VecModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  16. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  17. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  18. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. val description: String
    Definition Classes
    Doc2VecApproachAnnotatorApproach
  20. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  22. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  23. def explainParams(): String
    Definition Classes
    Params
  24. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  25. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  26. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  27. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  28. final def fit(dataset: Dataset[_]): Doc2VecModel
    Definition Classes
    AnnotatorApproach → Estimator
  29. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[Doc2VecModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  30. def fit(dataset: Dataset[_], paramMap: ParamMap): Doc2VecModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  31. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): Doc2VecModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  32. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  33. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  34. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  37. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  38. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  39. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  40. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  41. def getMaxIter: Int

  42. def getMaxSentenceLength: Int

  43. def getMinCount: Int

  44. def getNumPartitions: Int

  45. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  46. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  47. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  48. def getSeed: Int

  49. def getStepSize: Double

  50. def getStorageRef: String
    Definition Classes
    HasStorageRef
  51. def getVectorSize: Int

  52. def getWindowSize: Int

  53. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  54. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  55. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  56. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  57. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. val inputAnnotatorTypes: Array[AnnotatorType]

    Input Annotator Types: TOKEN

    Input Annotator Types: TOKEN

    Definition Classes
    Doc2VecApproachHasInputAnnotationCols
  59. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  60. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  61. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  62. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  63. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  64. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  65. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  66. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  73. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. val maxIter: IntParam

    Param for maximum number of iterations (>= 0) (Default: 1)

  78. val maxSentenceLength: IntParam

    Sets the maximum length (in words) of each sentence in the input data (Default: 1000).

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

  79. val minCount: IntParam

    The minimum number of times a token must appear to be included in the word2vec model's vocabulary.

    The minimum number of times a token must appear to be included in the word2vec model's vocabulary. Default: 5

  80. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  81. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  82. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  83. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. val numPartitions: IntParam

    Number of partitions for sentences of words (Default: 1).

  85. def onTrained(model: Doc2VecModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  86. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  87. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  88. val outputAnnotatorType: String

    Output Annotator Types: SENTENCE_EMBEDDINGS

    Output Annotator Types: SENTENCE_EMBEDDINGS

    Definition Classes
    Doc2VecApproachHasOutputAnnotatorType
  89. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  90. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  91. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  92. val seed: IntParam

    Random seed for shuffling the dataset (Default: 44)

  93. def set[T](feature: StructFeature[T], value: T): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: SetFeature[T], value: Set[T]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def set[T](feature: ArrayFeature[T], value: Array[T]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def set(paramPair: ParamPair[_]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. final def set[T](param: Param[T], value: T): Doc2VecApproach.this.type
    Definition Classes
    Params
  100. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. final def setDefault(paramPairs: ParamPair[_]*): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def setDefault[T](param: Param[T], value: T): Doc2VecApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def setInputCols(value: String*): Doc2VecApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  107. def setInputCols(value: Array[String]): Doc2VecApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  108. def setLazyAnnotator(value: Boolean): Doc2VecApproach.this.type
    Definition Classes
    CanBeLazy
  109. def setMaxIter(value: Int): Doc2VecApproach.this.type

  110. def setMaxSentenceLength(value: Int): Doc2VecApproach.this.type

  111. def setMinCount(value: Int): Doc2VecApproach.this.type

  112. def setNumPartitions(value: Int): Doc2VecApproach.this.type

  113. final def setOutputCol(value: String): Doc2VecApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  114. def setSeed(value: Int): Doc2VecApproach.this.type

  115. def setStepSize(value: Double): Doc2VecApproach.this.type

  116. def setStorageRef(value: String): Doc2VecApproach.this.type
    Definition Classes
    HasStorageRef
  117. def setVectorSize(value: Int): Doc2VecApproach.this.type

  118. def setWindowSize(value: Int): Doc2VecApproach.this.type

  119. val stepSize: DoubleParam

    Param for Step size to be used for each iteration of optimization (> 0) (Default: 0.025).

  120. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  121. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  122. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  123. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): Doc2VecModel
    Definition Classes
    Doc2VecApproachAnnotatorApproach
  124. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    AnnotatorApproach → PipelineStage
  125. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  126. val uid: String
    Definition Classes
    Doc2VecApproach → Identifiable
  127. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  128. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  129. val vectorSize: IntParam

    The dimension of the code that you want to transform from words (Default: 100).

  130. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  131. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  132. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  133. val windowSize: IntParam

    The window size (context words from [-window, window]) (Default: 5)

  134. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasStorageRef

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[Doc2VecModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters