Packages

class GenericClassifierApproach extends AnnotatorApproach[GenericClassifierModel] with HandleExceptionParams with CheckLicense

Trains a TensorFlow model for generic classification of feature vectors. It takes FEATURE_VECTOR annotations from FeaturesAssembler as input, classifies them and outputs CATEGORY annotations. Please see the Parameters section for required training parameters.

For a more extensive example please see the Spark NLP Workshop.

Example

val features_asm = new FeaturesAssembler()
  .setInputCols(Array("feature_1", "feature_2", "...", "feature_n"))
  .setOutputCol("features")

val gen_clf = new GenericClassifierApproach()
  .setLabelColumn("target")
  .setInputCols("features")
  .setOutputCol("prediction")
  .setModelFile("/path/to/graph_file.pb")
  .setEpochsNumber(50)
  .setBatchSize(100)
  .setFeatureScaling("zscore")
  .setlearningRate(0.001f)
  .setFixImbalance(true)
  .setOutputLogsPath("logs")
  .setValidationSplit(0.2f) // keep 20% of the data for validation purposes

val pipeline = new Pipeline().setStages(Array(
  features_asm,
  gen_clf
))

val clf_model = pipeline.fit(data)
See also

GenericClassifierModel for the trained model

Linear Supertypes
CheckLicense, HandleExceptionParams, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
FinanceFewShotClassifierApproach, FinanceFewShotClassifierApproach, LegalFewShotClassifierApproach, LegalFewShotClassifierApproach, FewShotClassifierApproach, FewShotClassifierApproach, GenericLogRegClassifierApproach, GenericLogRegClassifierApproach, GenericSVMClassifierApproach, GenericSVMClassifierApproach, RelationExtractionApproach, RelationExtractionApproach
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Inherited
  1. GenericClassifierApproach
  2. CheckLicense
  3. HandleExceptionParams
  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 GenericClassifierApproach()
  2. new GenericClassifierApproach(uid: String)

    uid

    a unique identifier for the instantiated AnnotatorModel

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

  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]): Unit
    Definition Classes
    CheckLicense
  11. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  12. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  13. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  14. final def clear(param: Param[_]): GenericClassifierApproach.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. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. val description: String

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  20. 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
  21. val dropout: FloatParam

    Dropout coefficient

  22. val epochsN: IntParam

    Maximum number of epochs to train

  23. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  25. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  26. def explainParams(): String
    Definition Classes
    Params
  27. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  28. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  29. val featureScaling: Param[String]

    Feature scaling method.

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

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

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

  36. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  37. def getBatchSize: Int

    Batch size

  38. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  39. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  40. def getDropout: Float

    Dropout coefficient

  41. def getExistingLabels(): Array[String]
    Attributes
    protected
  42. def getFeatureScaling: String

    Get feature scaling method

  43. def getFixImbalance: Boolean

    Fix imbalance in training set

  44. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  45. def getLabelColumn: String

    Column with label per each document

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

    Learning Rate

  48. def getMaxEpochs: Int

    Maximum number of epochs to train

  49. def getModelFile: String

    Model file name

  50. def getMultiClass: Boolean

    Gets the model multi class prediction mode

  51. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  52. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  53. def getOutputLogsPath: String

    Get output logs path

  54. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  55. def getTFWrapper(): TensorflowWrapper
    Attributes
    protected
  56. 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.

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

    Input annotator type : FEATURE_VECTOR

    Input annotator type : FEATURE_VECTOR

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

    Column with label per each document

  69. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  70. val learningRate: FloatParam

    Learning Rate

  71. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  72. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  79. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. val modelFile: Param[String]

    Location of file of the model used for classification

  84. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  85. 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.

  86. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  87. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  90. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  91. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  92. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  93. 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).

  94. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  95. def resumeTraining: Boolean
    Attributes
    protected
  96. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  97. final def set(paramPair: ParamPair[_]): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. final def set[T](param: Param[T], value: T): GenericClassifierApproach.this.type
    Definition Classes
    Params
  100. def setBatchSize(batch: Int): GenericClassifierApproach.this.type

    Batch size

  101. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  102. final def setDefault[T](param: Param[T], value: T): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  103. def setDoExceptionHandling(value: Boolean): GenericClassifierApproach.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
  104. def setDropout(dropout: Float): GenericClassifierApproach.this.type

    Dropout coefficient

  105. def setEpochsNumber(epochs: Int): GenericClassifierApproach.this.type

    Maximum number of epochs to train

  106. def setFeatureScaling(featureScaling: String): GenericClassifierApproach.this.type

    Set the feature scaling method.

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

  107. def setFixImbalance(fix: Boolean): GenericClassifierApproach.this.type

    Fix imbalance of training set

  108. final def setInputCols(value: String*): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  109. def setInputCols(value: Array[String]): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  110. def setLabelColumn(column: String): GenericClassifierApproach.this.type

    Column with label per each document

  111. def setLazyAnnotator(value: Boolean): GenericClassifierApproach.this.type
    Definition Classes
    CanBeLazy
  112. def setModelFile(modelFile: String): GenericClassifierApproach.this.type

    Set the model file name

  113. def setMultiClass(value: Boolean): GenericClassifierApproach.this.type

    Sets the model in multi class prediction mode

  114. final def setOutputCol(value: String): GenericClassifierApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  115. def setOutputLogsPath(outputLogsPath: String): GenericClassifierApproach.this.type

    Set the output log path

  116. def setValidationSplit(validationSplit: Float): GenericClassifierApproach.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.

  117. def setlearningRate(lr: Float): GenericClassifierApproach.this.type

    Learning Rate

  118. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  119. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  120. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): GenericClassifierModel
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  121. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  122. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  123. val uid: String
    Definition Classes
    GenericClassifierApproach → Identifiable
  124. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  125. 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.

  126. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  128. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  129. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from CheckLicense

Inherited from HandleExceptionParams

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