Packages

class GenericClassifierApproach extends AnnotatorApproach[GenericClassifierModel] with Licensed

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
Licensed, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GenericClassifierApproach
  2. Licensed
  3. AnnotatorApproach
  4. CanBeLazy
  5. DefaultParamsWritable
  6. MLWritable
  7. HasOutputAnnotatorType
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. Estimator
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. 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. final def clear(param: Param[_]): GenericClassifierApproach.this.type
    Definition Classes
    Params
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  12. final def copy(extra: ParamMap): Estimator[GenericClassifierModel]
    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

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  16. val dropout: FloatParam

    Dropout coefficient

  17. val epochsN: IntParam

    Maximum number of epochs to train

  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  21. def explainParams(): String
    Definition Classes
    Params
  22. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  24. val featureScaling: Param[String]

    Feature scaling method.

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

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

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

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

    Batch size

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

    Dropout coefficient

  36. def getFeatureScaling: String

    Get feature scaling method

  37. def getFixImbalance: Boolean

    Fix imbalance in training set

  38. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  39. def getLabelColumn: String

    Column with label per each document

  40. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  41. def getLearningRate: Float

    Learning Rate

  42. def getMaxEpochs: Int

    Maximum number of epochs to train

  43. def getModelFile: String

    Model file name

  44. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  45. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  46. def getOutputLogsPath: String

    Get output logs path

  47. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  48. 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.

  49. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  50. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  51. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  52. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  53. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : FEATURE_VECTOR

    Input annotator type : FEATURE_VECTOR

    Definition Classes
    GenericClassifierApproach → HasInputAnnotationCols
  55. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  56. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  57. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  58. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  59. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  60. val labelColumn: Param[String]

    Column with label per each document

  61. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  62. val learningRate: FloatParam

    Learning Rate

  63. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. val modelFile: Param[String]

    Location of file of the model used for classification

  76. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  77. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  78. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  79. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  80. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  81. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

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

    Path to folder to output logs.

    Path to folder to output logs. If no path is specified, no logs are generated

  84. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  85. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  86. final def set(paramPair: ParamPair[_]): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  87. final def set(param: String, value: Any): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  88. final def set[T](param: Param[T], value: T): GenericClassifierApproach.this.type
    Definition Classes
    Params
  89. def setBatchSize(batch: Int): GenericClassifierApproach.this.type

    Batch size

  90. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  91. final def setDefault[T](param: Param[T], value: T): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  92. def setDropout(dropout: Float): GenericClassifierApproach.this.type

    Dropout coefficient

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

    Maximum number of epochs to train

  94. 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)

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

    Fix imbalance of training set

  96. final def setInputCols(value: String*): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  97. final def setInputCols(value: Array[String]): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  98. def setLabelColumn(column: String): GenericClassifierApproach.this.type

    Column with label per each document

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

    Set the model file name

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

    Set the output log path

  103. 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.

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

    Learning Rate

  105. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  106. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  107. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): GenericClassifierModel
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  108. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  109. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  110. val uid: String
    Definition Classes
    GenericClassifierApproach → Identifiable
  111. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  112. 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.

  113. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  115. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  116. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from Licensed

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