Packages

class MultiColumnAssembler extends AnnotatorModel[MultiColumnAssembler] with HasSimpleAnnotate[MultiColumnAssembler] with HasMultipleInputAnnotationCols

Merges multiple annotation columns into a single annotation column.

This is useful when multiple annotators produce separate annotation columns (e.g., document_text, document_table from ReaderAssembler) and a downstream annotator (e.g., AutoGGUFVisionModel) expects a single input column containing all annotations.

Annotations from all input columns are collected and concatenated into the output column. The output annotator type defaults to DOCUMENT but can be configured. Each annotation's metadata is preserved, and a source_column key is added to track the original column name.

Note: All input columns must use the Annotation schema (i.e., Annotation.dataType). Columns using AnnotationImage.dataType (e.g., IMAGE-typed columns from ReaderAssembler) are not supported and will cause a validation error.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.{MultiColumnAssembler, DocumentAssembler}
import org.apache.spark.ml.Pipeline

val documentAssembler1 = new DocumentAssembler()
  .setInputCol("text1")
  .setOutputCol("document_text")

val documentAssembler2 = new DocumentAssembler()
  .setInputCol("text2")
  .setOutputCol("document_table")

val multiColumnAssembler = new MultiColumnAssembler()
  .setInputCols("document_text", "document_table")
  .setOutputCol("merged_document")

val data = Seq(("Hello world", "Name | Age\nJohn | 30"))
  .toDF("text1", "text2")

val pipeline = new Pipeline()
  .setStages(Array(documentAssembler1, documentAssembler2, multiColumnAssembler))
  .fit(data)

val result = pipeline.transform(data)
result.selectExpr("merged_document.result").show(false)
Linear Supertypes
HasMultipleInputAnnotationCols, HasSimpleAnnotate[MultiColumnAssembler], AnnotatorModel[MultiColumnAssembler], CanBeLazy, RawAnnotator[MultiColumnAssembler], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[MultiColumnAssembler], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MultiColumnAssembler
  2. HasMultipleInputAnnotationCols
  3. HasSimpleAnnotate
  4. AnnotatorModel
  5. CanBeLazy
  6. RawAnnotator
  7. HasOutputAnnotationCol
  8. HasInputAnnotationCols
  9. HasOutputAnnotatorType
  10. ParamsAndFeaturesWritable
  11. HasFeatures
  12. DefaultParamsWritable
  13. MLWritable
  14. Model
  15. Transformer
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Serializable
  21. Identifiable
  22. AnyRef
  23. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultiColumnAssembler()
  2. new MultiColumnAssembler(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 annotations from all input columns and merges them into a single sequence.

    Takes annotations from all input columns and merges them into a single sequence.

    This is a fallback used when dfAnnotate is not invoked directly. In normal pipeline execution, dfAnnotate is used instead so that source column names can be tracked.

    annotations

    Annotations from all input columns, flattened

    returns

    Merged annotations with output annotator type applied

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

    requirement for annotators copies

    requirement for annotators copies

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

    Overrides the default dfAnnotate to preserve per-column source information.

    Overrides the default dfAnnotate to preserve per-column source information.

    The default HasSimpleAnnotate.dfAnnotate flattens all input columns before calling annotate, losing track of which annotation came from which column. This override zips the per-column annotation sequences with getInputCols to tag each annotation's metadata with its true source_column name.

    returns

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

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

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    MultiColumnAssembler → 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. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  42. def getOutputAsAnnotatorType: String

  43. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  44. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  45. def getSortByBegin: Boolean

  46. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  47. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  48. def hasParent: Boolean
    Definition Classes
    Model
  49. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. val inputAnnotatorType: String

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

    Definition Classes
    MultiColumnAssembler → HasMultipleInputAnnotationCols
  53. lazy val inputAnnotatorTypes: Array[String]

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

    Definition Classes
    HasMultipleInputAnnotationCols → HasInputAnnotationCols
  54. 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
  55. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  56. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  57. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  58. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  59. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  60. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  73. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  74. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  75. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  76. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  77. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  78. val outputAnnotatorType: AnnotatorType

    Output annotator types: DOCUMENT

    Output annotator types: DOCUMENT

    Definition Classes
    MultiColumnAssembler → HasOutputAnnotatorType
  79. val outputAsAnnotatorType: Param[String]

    The annotator type to use for the output column (Default: document).

    The annotator type to use for the output column (Default: document).

    This can be changed to match the expected input type of downstream annotators.

  80. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  81. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  82. var parent: Estimator[MultiColumnAssembler]
    Definition Classes
    Model
  83. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  84. def set[T](feature: StructFeature[T], value: T): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  85. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  86. def set[T](feature: SetFeature[T], value: Set[T]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  87. def set[T](feature: ArrayFeature[T], value: Array[T]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  88. final def set(paramPair: ParamPair[_]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    Params
  89. final def set(param: String, value: Any): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    Params
  90. final def set[T](param: Param[T], value: T): MultiColumnAssembler.this.type
    Definition Classes
    Params
  91. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. final def setDefault(paramPairs: ParamPair[_]*): MultiColumnAssembler.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. final def setDefault[T](param: Param[T], value: T): MultiColumnAssembler.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  97. def setExtraInputCols(value: Array[String]): MultiColumnAssembler.this.type
    Definition Classes
    HasInputAnnotationCols
  98. def setInputCols(value: Array[String]): MultiColumnAssembler.this.type

    Columns that contain annotations necessary to run this annotator AnnotatorType is the same for all input columns in that annotator.

    Columns that contain annotations necessary to run this annotator AnnotatorType is the same for all input columns in that annotator.

    Definition Classes
    HasMultipleInputAnnotationCols → HasInputAnnotationCols
  99. final def setInputCols(value: String*): MultiColumnAssembler.this.type
    Definition Classes
    HasInputAnnotationCols
  100. def setLazyAnnotator(value: Boolean): MultiColumnAssembler.this.type
    Definition Classes
    CanBeLazy
  101. def setOutputAsAnnotatorType(value: String): MultiColumnAssembler.this.type

  102. final def setOutputCol(value: String): MultiColumnAssembler.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  103. def setParent(parent: Estimator[MultiColumnAssembler]): MultiColumnAssembler
    Definition Classes
    Model
  104. def setSortByBegin(value: Boolean): MultiColumnAssembler.this.type

  105. val sortByBegin: BooleanParam

    Whether to sort merged annotations by their begin position (Default: false).

    Whether to sort merged annotations by their begin position (Default: false).

    When false, annotations are ordered by input column order (all from first column, then second, etc.). When true, annotations from all columns are interleaved by their begin position.

  106. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  107. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  108. 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
  109. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  110. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  111. 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
  112. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  113. val uid: String
    Definition Classes
    MultiColumnAssembler → Identifiable
  114. def validate(schema: StructType): Boolean

    Override validation to accept any annotation type on input columns, not just DOCUMENT.

    Override validation to accept any annotation type on input columns, not just DOCUMENT. This allows merging columns with different annotator types (e.g., DOCUMENT + TABLE). Columns must still have the annotatorType metadata key. IMAGE columns are rejected because they use AnnotationImage.dataType which is incompatible with the standard Annotation schema.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    MultiColumnAssembler → RawAnnotator
  115. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  116. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  117. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  118. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  119. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasMultipleInputAnnotationCols

Inherited from HasSimpleAnnotate[MultiColumnAssembler]

Inherited from AnnotatorModel[MultiColumnAssembler]

Inherited from CanBeLazy

Inherited from RawAnnotator[MultiColumnAssembler]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[MultiColumnAssembler]

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