object LegalFewShotClassifierApproach extends LegalFewShotClassifierApproach

Linear Supertypes
LegalFewShotClassifierApproach, FewShotClassifierApproach, GenericLogRegClassifierApproach, GenericClassifierApproach, CheckLicense, HandleExceptionParams, GenericClassifierParams, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LegalFewShotClassifierApproach
  2. LegalFewShotClassifierApproach
  3. FewShotClassifierApproach
  4. GenericLogRegClassifierApproach
  5. GenericClassifierApproach
  6. CheckLicense
  7. HandleExceptionParams
  8. GenericClassifierParams
  9. AnnotatorApproach
  10. CanBeLazy
  11. DefaultParamsWritable
  12. MLWritable
  13. HasOutputAnnotatorType
  14. HasOutputAnnotationCol
  15. HasInputAnnotationCols
  16. Estimator
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

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]): GenericClassifierModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val batchSize: IntParam

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  9. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  10. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String], metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  11. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  12. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  13. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  14. final def clear(param: Param[_]): LegalFewShotClassifierApproach.this.type
    Definition Classes
    Params
  15. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  16. final def copy(extra: ParamMap): Estimator[GenericClassifierModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  17. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  18. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

    Definition Classes
    GenericClassifierParams
  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
    GenericClassifierApproach → AnnotatorApproach
  21. val doExceptionHandling: BooleanParam

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  22. val dropout: FloatParam

    Dropout coefficient.

    Dropout coefficient.

    It is applied to the output of the last hidden layer of the graph. Graphs without hidden layer (GenericLogRegClassifier, FewShotClassifier, GenericSVMClassifier) apply it directly to the input feature vector, so a value greater than 0 drops input features there: set it to 0 for those annotators unless input dropout is what you want.

    Definition Classes
    GenericClassifierParams
  23. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  27. def explainParams(): String
    Definition Classes
    Params
  28. final val extraInputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  29. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  30. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  31. val featureScaling: Param[String]

    Feature scaling method.

    Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierParams
  32. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  33. final def fit(dataset: Dataset[_]): GenericClassifierModel
    Definition Classes
    AnnotatorApproach → Estimator
  34. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GenericClassifierModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  35. def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  36. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  37. val fixImbalance: BooleanParam

    Fix the imbalance in the training set by replicating examples of under represented categories.

    Fix the imbalance in the training set by replicating examples of under represented categories.

    The class weights are only used by the loss of graphs with a softmax output (the GenericClassifier/RelationExtraction family). Graphs with a sigmoid output (logistic regression, SVM) compute their loss without them, so this parameter has no effect there.

    Definition Classes
    GenericClassifierParams
  38. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  39. def getBatchSize: Int

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  40. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  42. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  44. def getExistingLabels(): Array[String]
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  45. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierParams
  46. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierParams
  47. def getGraphFolder: String

    Folder with the TensorFlow graphs to choose the model graph from

    Folder with the TensorFlow graphs to choose the model graph from

    Definition Classes
    GenericClassifierParams
  48. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  49. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  50. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  51. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  52. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  53. def getModelFile: String

    Path of the TensorFlow graph to train with, empty when it is resolved automatically

    Path of the TensorFlow graph to train with, empty when it is resolved automatically

    Definition Classes
    GenericClassifierParams
  54. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierParams
  55. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  56. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  57. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierParams
  58. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  59. def getTFWrapper(inputDim: Int, outputDim: Int): TensorflowWrapper
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  60. 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.

    Definition Classes
    GenericClassifierParams
  61. def graphFamily: DenseClassifierGraphFamily

    Logistic regression graphs: no hidden layer, sigmoid output.

    Logistic regression graphs: no hidden layer, sigmoid output.

    Attributes
    protected
    Definition Classes
    GenericLogRegClassifierApproach → GenericClassifierApproach
  62. val graphFolder: Param[String]

    Folder with TensorFlow graphs to choose the model graph from.

    Folder with TensorFlow graphs to choose the model graph from.

    When it is not set, the graphs embedded in the library are used. It is ignored when modelFile is set explicitly.

    Definition Classes
    GenericClassifierParams
  63. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  64. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  65. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  66. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator types : SENTENCE_EMBEDDINGS

    Input annotator types : SENTENCE_EMBEDDINGS

    Definition Classes
    FewShotClassifierApproach → GenericLogRegClassifierApproach → GenericClassifierApproach → HasInputAnnotationCols
  69. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  70. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  71. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  72. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  73. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  74. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  75. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  76. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  77. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  78. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  85. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. val modelFile: Param[String]

    Path of the TensorFlow graph to train with.

    Path of the TensorFlow graph to train with.

    Empty by default: a graph with suitable dimensions is then selected automatically, either from graphFolder or from the graphs embedded in the library. Set it to train with one specific graph.

    Definition Classes
    GenericClassifierParams
  90. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  91. val multiClass: BooleanParam

    If multiClass is set, the model will return all the labels with corresponding scores.

    If multiClass is set, the model will return all the labels with corresponding scores. By default, multiClass is false.

    Definition Classes
    GenericClassifierParams
  92. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  93. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  94. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  95. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  96. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  97. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  98. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  99. val outputLogsPath: Param[String]

    Folder path to save training logs.

    Folder path to save training logs. If no path is specified, the logs won't be stored in disk. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    Definition Classes
    GenericClassifierParams
  100. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  101. def resolveGraphFile(inputDim: Int, outputDim: Int): String

    Resolves the graph to train with.

    Resolves the graph to train with.

    modelFile wins when it has been set explicitly, otherwise the best graph of graphFamily is selected, either from graphFolder or from the graphs embedded in the library.

    inputDim

    length of the feature vectors of the training dataset

    outputDim

    number of classes of the training dataset

    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  102. def resumeTraining: Boolean
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  103. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  104. final def set(paramPair: ParamPair[_]): LegalFewShotClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def set(param: String, value: Any): LegalFewShotClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def set[T](param: Param[T], value: T): LegalFewShotClassifierApproach.this.type
    Definition Classes
    Params
  107. def setBatchSize(batch: Int): LegalFewShotClassifierApproach.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  108. def setDatasetInfo(value: String): LegalFewShotClassifierApproach.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  109. final def setDefault(paramPairs: ParamPair[_]*): LegalFewShotClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  110. final def setDefault[T](param: Param[T], value: T): LegalFewShotClassifierApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  111. def setDoExceptionHandling(value: Boolean): LegalFewShotClassifierApproach.this.type

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  112. def setDropout(dropout: Float): LegalFewShotClassifierApproach.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  113. def setEpochsNumber(epochs: Int): LegalFewShotClassifierApproach.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  114. def setExtraInputCols(value: Array[String]): LegalFewShotClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  115. def setFeatureScaling(featureScaling: String): LegalFewShotClassifierApproach.this.type

    Set the feature scaling method.

    Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierParams
  116. def setFixImbalance(fix: Boolean): LegalFewShotClassifierApproach.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierParams
  117. def setGraphFolder(path: String): LegalFewShotClassifierApproach.this.type

    Set the folder with the TensorFlow graphs to choose the model graph from.

    Set the folder with the TensorFlow graphs to choose the model graph from. Ignored when modelFile is set.

    Definition Classes
    GenericClassifierParams
  118. final def setInputCols(value: String*): LegalFewShotClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  119. def setInputCols(value: Array[String]): LegalFewShotClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  120. def setLabelColumn(column: String): LegalFewShotClassifierApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  121. def setLazyAnnotator(value: Boolean): LegalFewShotClassifierApproach.this.type
    Definition Classes
    CanBeLazy
  122. def setModelFile(modelFile: String): LegalFewShotClassifierApproach.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierParams
  123. def setMultiClass(value: Boolean): LegalFewShotClassifierApproach.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierParams
  124. final def setOutputCol(value: String): LegalFewShotClassifierApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  125. def setOutputLogsPath(outputLogsPath: String): LegalFewShotClassifierApproach.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierParams
  126. def setValidationSplit(validationSplit: Float): LegalFewShotClassifierApproach.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.

    Definition Classes
    GenericClassifierParams
  127. def setlearningRate(lr: Float): LegalFewShotClassifierApproach.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  128. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  129. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  130. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): GenericLogRegClassifierModel
    Definition Classes
    LegalFewShotClassifierApproach → FewShotClassifierApproach → GenericLogRegClassifierApproach → GenericClassifierApproach → AnnotatorApproach
  131. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  132. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  133. val uid: String
    Definition Classes
    LegalFewShotClassifierApproach → FewShotClassifierApproach → GenericLogRegClassifierApproach → GenericClassifierApproach → Identifiable
  134. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  135. val validationSplit: FloatParam

    The proportion of training dataset to be used as validation set.

    The proportion of training dataset to be used as validation set.

    The model will be validated against this dataset on each Epoch and will not be used for training. The value should be between 0.0 and 1.0.

    Definition Classes
    GenericClassifierParams
  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
    DefaultParamsWritable → MLWritable

Inherited from LegalFewShotClassifierApproach

Inherited from FewShotClassifierApproach

Inherited from GenericLogRegClassifierApproach

Inherited from GenericClassifierApproach

Inherited from CheckLicense

Inherited from HandleExceptionParams

Inherited from GenericClassifierParams

Inherited from AnnotatorApproach[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[GenericClassifierModel]

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