Packages

class RegexTokenizer extends AnnotatorModel[RegexTokenizer] with HasSimpleAnnotate[RegexTokenizer]

A tokenizer that splits text by a regex pattern.

The pattern needs to be set with setPattern and this sets the delimiting pattern or how the tokens should be split. By default this pattern is \s+ which means that tokens should be split by 1 or more whitespace characters.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.RegexTokenizer
import org.apache.spark.ml.Pipeline

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

val regexTokenizer = new RegexTokenizer()
  .setInputCols("document")
  .setOutputCol("regexToken")
  .setToLowercase(true)
  .setPattern("\\s+")

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

val data = Seq("This is my first sentence.\nThis is my second.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("regexToken.result").show(false)
+-------------------------------------------------------+
|result                                                 |
+-------------------------------------------------------+
|[this, is, my, first, sentence., this, is, my, second.]|
+-------------------------------------------------------+
Linear Supertypes
HasSimpleAnnotate[RegexTokenizer], AnnotatorModel[RegexTokenizer], CanBeLazy, RawAnnotator[RegexTokenizer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[RegexTokenizer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. RegexTokenizer
  2. HasSimpleAnnotate
  3. AnnotatorModel
  4. CanBeLazy
  5. RawAnnotator
  6. HasOutputAnnotationCol
  7. HasInputAnnotationCols
  8. HasOutputAnnotatorType
  9. ParamsAndFeaturesWritable
  10. HasFeatures
  11. DefaultParamsWritable
  12. MLWritable
  13. Model
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. 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 _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    RegexTokenizer → HasSimpleAnnotate
  12. def applyTrimPolicies(inputTokSentences: Seq[TokenizedSentence], trimWhitespace: Boolean, preservePosition: Boolean): Seq[TokenizedSentence]

    This func applies policies for token trimming when activated.

    This func applies policies for token trimming when activated.

    inputTokSentences

    input token sentences

    trimWhitespace

    policy to trim whitespaces in tokens

    preservePosition

    policy to preserve indexing in tokens

    returns

    Seq of TokenizedSentence objects after applied policies transformations

  13. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. final def clear(param: Param[_]): RegexTokenizer.this.type
    Definition Classes
    Params
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. def copy(extra: ParamMap): RegexTokenizer

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. def dfAnnotate: UserDefinedFunction

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    HasSimpleAnnotate
  22. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  24. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  25. def explainParams(): String
    Definition Classes
    Params
  26. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  27. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  29. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  30. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  31. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  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 getMaxLength: Int

  42. def getMinLength: Int

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  45. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  46. def getPattern: String

  47. def getPositionalMask: Boolean

  48. def getPreservePosition: Boolean

  49. def getToLowercase: Boolean

  50. def getTrimWhitespace: Boolean

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

    Input annotator type: DOCUMENT

    Input annotator type: DOCUMENT

    Definition Classes
    RegexTokenizer → HasInputAnnotationCols
  58. 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
  59. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  60. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  61. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  62. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  63. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  64. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. val maxLength: IntParam

    Maximum token length, greater than or equal to 1.

  77. val minLength: IntParam

    Minimum token length, greater than or equal to 0 (Default: 1).

    Minimum token length, greater than or equal to 0 (Default: 1). Default is 1, to avoid returning empty strings.

  78. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  79. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  80. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  81. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  82. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  83. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  84. val outputAnnotatorType: AnnotatorType

    Output annotator type: TOKEN

    Output annotator type: TOKEN

    Definition Classes
    RegexTokenizer → HasOutputAnnotatorType
  85. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  86. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  87. var parent: Estimator[RegexTokenizer]
    Definition Classes
    Model
  88. val pattern: Param[String]

    Regex pattern used to match delimiters (Default: "\\s+")

  89. val positionalMask: BooleanParam

    Indicates whether to apply the regex tokenization using a positional mask to guarantee the incremental progression (Default: false).

  90. val preservePosition: BooleanParam

    Indicates whether to use a preserve initial indexes before eventual whitespaces removal in tokens.

    Indicates whether to use a preserve initial indexes before eventual whitespaces removal in tokens. (Default: false).

  91. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  92. def set[T](feature: StructFeature[T], value: T): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[T](feature: SetFeature[T], value: Set[T]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: ArrayFeature[T], value: Array[T]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. final def set(paramPair: ParamPair[_]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  97. final def set(param: String, value: Any): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set[T](param: Param[T], value: T): RegexTokenizer.this.type
    Definition Classes
    Params
  99. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. final def setDefault(paramPairs: ParamPair[_]*): RegexTokenizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. final def setDefault[T](param: Param[T], value: T): RegexTokenizer.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  105. final def setInputCols(value: String*): RegexTokenizer.this.type
    Definition Classes
    HasInputAnnotationCols
  106. def setInputCols(value: Array[String]): RegexTokenizer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  107. def setLazyAnnotator(value: Boolean): RegexTokenizer.this.type
    Definition Classes
    CanBeLazy
  108. def setMaxLength(value: Int): RegexTokenizer.this.type

  109. def setMinLength(value: Int): RegexTokenizer.this.type

  110. final def setOutputCol(value: String): RegexTokenizer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  111. def setParent(parent: Estimator[RegexTokenizer]): RegexTokenizer
    Definition Classes
    Model
  112. def setPattern(value: String): RegexTokenizer.this.type

  113. def setPositionalMask(value: Boolean): RegexTokenizer.this.type

  114. def setPreservePosition(value: Boolean): RegexTokenizer.this.type

  115. def setToLowercase(value: Boolean): RegexTokenizer.this.type

  116. def setTrimWhitespace(value: Boolean): RegexTokenizer.this.type

  117. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  118. def tag(sentences: Seq[Sentence]): Seq[TokenizedSentence]

    This func generates a Seq of TokenizedSentences from a Seq of Sentences.

    This func generates a Seq of TokenizedSentences from a Seq of Sentences.

    sentences

    to tag

    returns

    Seq of TokenizedSentence objects

  119. def tagWithPositionalMask(sentences: Seq[Sentence]): Seq[TokenizedSentence]

    This func generates a Seq of TokenizedSentences from a Seq of Sentences preserving positional progression

    This func generates a Seq of TokenizedSentences from a Seq of Sentences preserving positional progression

    sentences

    to tag

    returns

    Seq of TokenizedSentence objects

  120. val toLowercase: BooleanParam

    Indicates whether to convert all characters to lowercase before tokenizing (Default: false).

  121. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  122. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  123. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  124. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  125. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

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

    Definition Classes
    RawAnnotator → PipelineStage
  126. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  127. val trimWhitespace: BooleanParam

    Indicates whether to use a trimWhitespace flag to remove whitespaces from identified tokens.

    Indicates whether to use a trimWhitespace flag to remove whitespaces from identified tokens. (Default: false).

  128. val uid: String
    Definition Classes
    RegexTokenizer → Identifiable
  129. 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
    RawAnnotator
  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. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  134. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasSimpleAnnotate[RegexTokenizer]

Inherited from AnnotatorModel[RegexTokenizer]

Inherited from CanBeLazy

Inherited from RawAnnotator[RegexTokenizer]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[RegexTokenizer]

Inherited from Transformer

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