c

com.johnsnowlabs.nlp.annotators.classifier.dl

MultiClassifierDLApproach

class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

MultiClassifierDL is a Multi-label Text Classification. MultiClassifierDL uses a Bidirectional GRU with Convolution model that we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings

In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y). https://en.wikipedia.org/wiki/Multi-label_classification

NOTE: This annotator accepts an array of labels in type of String. NOTE: UniversalSentenceEncoder and SentenceEmbeddings can be used for the inputCol

See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLTestSpec.scala for further reference on how to use this API

Linear Supertypes
ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[MultiClassifierDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[MultiClassifierDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MultiClassifierDLApproach
  2. ParamsAndFeaturesWritable
  3. HasFeatures
  4. AnnotatorApproach
  5. CanBeLazy
  6. DefaultParamsWritable
  7. MLWritable
  8. HasOutputAnnotatorType
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultiClassifierDLApproach()
  2. new MultiClassifierDLApproach(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]): MultiClassifierDLModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val batchSize: IntParam

    Batch size

  12. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    MultiClassifierDLApproach → AnnotatorApproach
  13. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  14. final def clear(param: Param[_]): MultiClassifierDLApproach.this.type
    Definition Classes
    Params
  15. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  16. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  17. final def copy(extra: ParamMap): Estimator[MultiClassifierDLModel]
    Definition Classes
    AnnotatorApproach → Estimator → 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. val description: String

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    MultiClassifierDLApproach → AnnotatorApproach
  21. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder

  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. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  27. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  28. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  29. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  30. final def fit(dataset: Dataset[_]): MultiClassifierDLModel
    Definition Classes
    AnnotatorApproach → Estimator
  31. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[MultiClassifierDLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  32. def fit(dataset: Dataset[_], paramMap: ParamMap): MultiClassifierDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  33. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): MultiClassifierDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  34. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  39. def getBatchSize: Int

    Batch size

  40. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. def getConfigProtoBytes: Option[Array[Byte]]

    Tensorflow config Protobytes passed to the TF session

  42. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getEnableOutputLogs: Boolean

    Whether to output to annotators log folder

  44. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  45. def getLabelColumn: String

    Column with label per each document

  46. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  47. def getLr: Float

    Learning Rate

  48. def getMaxEpochs: Int

    Maximum number of epochs to train

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  51. def getOutputLogsPath: String
  52. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  53. def getShufflePerEpoch: Boolean

    Max sequence length to feed into TensorFlow

  54. def getThreshold: Float

    The minimum threshold for each label to be accepted.

    The minimum threshold for each label to be accepted. Default is 0.5

  55. def getValidationSplit: Float

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

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

    Input annotator type : SENTENCE_EMBEDDINGS

    Input annotator type : SENTENCE_EMBEDDINGS

    Definition Classes
    MultiClassifierDLApproach → HasInputAnnotationCols
  62. 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
  63. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  64. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  65. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  66. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. val labelColumn: Param[String]

    Column with label per each document

  68. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  69. def loadSavedModel(): TensorflowWrapper
  70. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. val lr: FloatParam

    Learning Rate

  83. val maxEpochs: IntParam

    Maximum number of epochs to train

  84. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  85. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  86. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  87. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. def onTrained(model: MultiClassifierDLModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  89. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  90. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    MultiClassifierDLApproach → HasOutputAnnotatorType
  91. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  92. val outputLogsPath: Param[String]
  93. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  94. val randomSeed: IntParam

    Random seed

  95. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  96. def set[T](feature: StructFeature[T], value: T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. def set[T](feature: SetFeature[T], value: Set[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. def set[T](feature: ArrayFeature[T], value: Array[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. final def set(paramPair: ParamPair[_]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  101. final def set(param: String, value: Any): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  102. final def set[T](param: Param[T], value: T): MultiClassifierDLApproach.this.type
    Definition Classes
    Params
  103. def setBatchSize(batch: Int): MultiClassifierDLApproach.this.type

    Batch size

  104. def setConfigProtoBytes(bytes: Array[Int]): MultiClassifierDLApproach.this.type

    Tensorflow config Protobytes passed to the TF session

  105. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  107. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. final def setDefault(paramPairs: ParamPair[_]*): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  110. final def setDefault[T](param: Param[T], value: T): MultiClassifierDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  111. def setEnableOutputLogs(enableOutputLogs: Boolean): MultiClassifierDLApproach.this.type

    Whether to output to annotators log folder

  112. final def setInputCols(value: String*): MultiClassifierDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  113. final def setInputCols(value: Array[String]): MultiClassifierDLApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  114. def setLabelColumn(column: String): MultiClassifierDLApproach.this.type

    Column with label per each document

  115. def setLazyAnnotator(value: Boolean): MultiClassifierDLApproach.this.type
    Definition Classes
    CanBeLazy
  116. def setLr(lr: Float): MultiClassifierDLApproach.this.type

    Learning Rate

  117. def setMaxEpochs(epochs: Int): MultiClassifierDLApproach.this.type

    Maximum number of epochs to train

  118. final def setOutputCol(value: String): MultiClassifierDLApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  119. def setOutputLogsPath(path: String): MultiClassifierDLApproach.this.type

    outputLogsPath

  120. def setShufflePerEpoch(value: Boolean): MultiClassifierDLApproach.this.type

    shufflePerEpoch

  121. def setThreshold(threshold: Float): MultiClassifierDLApproach.this.type

    The minimum threshold for each label to be accepted.

    The minimum threshold for each label to be accepted. Default is 0.5

  122. def setValidationSplit(validationSplit: Float): MultiClassifierDLApproach.this.type

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

  123. def setVerbose(verbose: Level): MultiClassifierDLApproach.this.type

    Level of verbosity during training

  124. def setVerbose(verbose: Int): MultiClassifierDLApproach.this.type

    Level of verbosity during training

  125. val shufflePerEpoch: BooleanParam

    Whether to shuffle the training data on each Epoch

  126. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  127. val threshold: FloatParam

    The minimum threshold for each label to be accepted.

    The minimum threshold for each label to be accepted. Default is 0.5

  128. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  129. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): MultiClassifierDLModel
    Definition Classes
    MultiClassifierDLApproach → AnnotatorApproach
  130. 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
  131. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  132. val uid: String
    Definition Classes
    MultiClassifierDLApproach → Identifiable
  133. 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
  134. val validationSplit: FloatParam

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

  135. val verbose: IntParam

    Level of verbosity during training

  136. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  137. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  138. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  139. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from AnnotatorApproach[MultiClassifierDLModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[MultiClassifierDLModel]

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

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters