Packages

class Normalizer extends AnnotatorApproach[NormalizerModel]

Annotator that cleans out tokens. Requires stems, hence tokens. Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary

For extended examples of usage, see the Examples.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.{Normalizer, Tokenizer}
import org.apache.spark.ml.Pipeline
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

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

val normalizer = new Normalizer()
  .setInputCols("token")
  .setOutputCol("normalized")
  .setLowercase(true)
  .setCleanupPatterns(Array("""[^\w\d\s]""")) // remove punctuations (keep alphanumeric chars)
// if we don't set CleanupPatterns, it will only keep alphabet letters ([^A-Za-z])

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

val data = Seq("John and Peter are brothers. However they don't support each other that much.")
  .toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("normalized.result").show(truncate = false)
+----------------------------------------------------------------------------------------+
|result                                                                                  |
+----------------------------------------------------------------------------------------+
|[john, and, peter, are, brothers, however, they, dont, support, each, other, that, much]|
+----------------------------------------------------------------------------------------+
Linear Supertypes
AnnotatorApproach[NormalizerModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[NormalizerModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. Normalizer
  2. AnnotatorApproach
  3. CanBeLazy
  4. DefaultParamsWritable
  5. MLWritable
  6. HasOutputAnnotatorType
  7. HasOutputAnnotationCol
  8. HasInputAnnotationCols
  9. Estimator
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

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

    uid

    required internal uid for saving annotator

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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): NormalizerModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  8. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  9. val cleanupPatterns: StringArrayParam

    Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]"))

  10. final def clear(param: Param[_]): Normalizer.this.type
    Definition Classes
    Params
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @HotSpotIntrinsicCandidate() @native()
  12. final def copy(extra: ParamMap): Estimator[NormalizerModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  14. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. val description: String

    Cleans out tokens

    Cleans out tokens

    Definition Classes
    Normalizer → AnnotatorApproach
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  18. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  19. def explainParams(): String
    Definition Classes
    Params
  20. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  22. final def fit(dataset: Dataset[_]): NormalizerModel
    Definition Classes
    AnnotatorApproach → Estimator
  23. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[NormalizerModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  24. def fit(dataset: Dataset[_], paramMap: ParamMap): NormalizerModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  25. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): NormalizerModel
    Definition Classes
    Estimator
    Annotations
    @varargs() @Since("2.0.0")
  26. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  27. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @HotSpotIntrinsicCandidate() @native()
  28. def getCleanupPatterns: Array[String]

    Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]"))

  29. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  30. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  31. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  32. def getLowercase: Boolean

    Whether to convert strings to lowercase (Default: false)

  33. def getMaxLength: Int

    Set the maximum allowed length for each token

  34. def getMinLength: Int

    Set the minimum allowed length for each token (Default: 0)

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  37. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  38. def getSlangMatchCase: Boolean

    Whether or not to be case sensitive to match slangs (Default: false)

  39. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  40. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @HotSpotIntrinsicCandidate() @native()
  42. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  43. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. val inputAnnotatorTypes: Array[String]

    Input Annotator Type : TOKEN

    Input Annotator Type : TOKEN

    Definition Classes
    Normalizer → HasInputAnnotationCols
  45. 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
  46. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  47. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  48. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  49. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  51. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  52. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  59. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val lowercase: BooleanParam

    Whether to convert strings to lowercase (Default: false)

  64. val maxLength: IntParam

    Set the maximum allowed length for each token

  65. val minLength: IntParam

    Set the minimum allowed length for each token (Default: 0)

  66. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  67. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  68. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @HotSpotIntrinsicCandidate() @native()
  69. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @HotSpotIntrinsicCandidate() @native()
  70. def onTrained(model: NormalizerModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  71. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  72. val outputAnnotatorType: AnnotatorType

    Output Annotator Type : TOKEN

    Output Annotator Type : TOKEN

    Definition Classes
    Normalizer → HasOutputAnnotatorType
  73. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  74. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  75. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @throws("If the input path already exists but overwrite is not enabled.") @Since("1.6.0")
  76. final def set(paramPair: ParamPair[_]): Normalizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. final def set(param: String, value: Any): Normalizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set[T](param: Param[T], value: T): Normalizer.this.type
    Definition Classes
    Params
  79. def setCleanupPatterns(value: Array[String]): Normalizer.this.type

    Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]"))

  80. final def setDefault(paramPairs: ParamPair[_]*): Normalizer.this.type
    Attributes
    protected
    Definition Classes
    Params
  81. final def setDefault[T](param: Param[T], value: T): Normalizer.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  82. final def setInputCols(value: String*): Normalizer.this.type
    Definition Classes
    HasInputAnnotationCols
  83. def setInputCols(value: Array[String]): Normalizer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  84. def setLazyAnnotator(value: Boolean): Normalizer.this.type
    Definition Classes
    CanBeLazy
  85. def setLowercase(value: Boolean): Normalizer.this.type

    Whether to convert strings to lowercase (Default: false)

  86. def setMaxLength(value: Int): Normalizer.this.type

    Set the maximum allowed length for each token

  87. def setMinLength(value: Int): Normalizer.this.type

    Set the minimum allowed length for each token (Default: 0)

  88. final def setOutputCol(value: String): Normalizer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  89. def setSlangDictionary(path: String, delimiter: String, readAs: Format = ReadAs.TEXT, options: Map[String, String] = Map("format" -> "text")): Normalizer.this.type

    Delimited file with list of custom words to be manually corrected

  90. def setSlangDictionary(value: ExternalResource): Normalizer.this.type

    Delimited file with list of custom words to be manually corrected

  91. def setSlangMatchCase(value: Boolean): Normalizer.this.type

    Whether or not to be case sensitive to match slangs (Default: false)

  92. val slangDictionary: ExternalResourceParam

    Delimited file with list of custom words to be manually corrected

  93. val slangMatchCase: BooleanParam

    Whether or not to be case sensitive to match slangs (Default: false)

  94. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  95. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  96. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): NormalizerModel
    Definition Classes
    Normalizer → AnnotatorApproach
  97. 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
  98. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  99. val uid: String
    Definition Classes
    Normalizer → Identifiable
  100. 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
  101. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  102. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  103. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  104. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from AnnotatorApproach[NormalizerModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[NormalizerModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

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