class GenericClassifierApproach extends AnnotatorApproach[GenericClassifierModel] with GenericClassifierParams 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, GenericClassifierParams, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
FinanceFewShotClassifierApproach, FinanceFewShotClassifierApproach, LegalFewShotClassifierApproach, LegalFewShotClassifierApproach, FewShotAssertionClassifierApproach, FewShotAssertionClassifierApproach, FewShotClassifierApproach, FewShotClassifierApproach, GenericLogRegClassifierApproach, GenericLogRegClassifierApproach, GenericSVMClassifierApproach, GenericSVMClassifierApproach, RelationExtractionApproach, RelationExtractionApproach
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Inherited
  1. GenericClassifierApproach
  2. CheckLicense
  3. HandleExceptionParams
  4. GenericClassifierParams
  5. AnnotatorApproach
  6. CanBeLazy
  7. DefaultParamsWritable
  8. MLWritable
  9. HasOutputAnnotatorType
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. 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

    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]): 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. 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

    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 def extractParamMap(): ParamMap
    Definition Classes
    Params
  29. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  30. val featureScaling: Param[String]

    Feature scaling method.

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

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

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

    Batch size

    Batch size

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

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

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

    Dropout coefficient

    Dropout coefficient

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

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierParams
  45. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierParams
  46. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  47. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  48. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  49. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  50. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  51. def getModelFile: String

    Model file name

    Model file name

    Definition Classes
    GenericClassifierParams
  52. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

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

    Get output logs path

    Get output logs path

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

    Input annotator type : FEATURE_VECTOR

    Input annotator type : FEATURE_VECTOR

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

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  71. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  72. val learningRate: FloatParam

    Learning Rate

    Learning Rate

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

    Location of file of the model used for classification

    Location of file of the model used for classification

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

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

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

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  103. def setDatasetInfo(value: String): GenericClassifierApproach.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  104. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def setDefault[T](param: Param[T], value: T): GenericClassifierApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  106. 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
  107. def setDropout(dropout: Float): GenericClassifierApproach.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  108. def setEpochsNumber(epochs: Int): GenericClassifierApproach.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  109. 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)

    Definition Classes
    GenericClassifierParams
  110. def setFixImbalance(fix: Boolean): GenericClassifierApproach.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierParams
  111. final def setInputCols(value: String*): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  112. def setInputCols(value: Array[String]): GenericClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  113. def setLabelColumn(column: String): GenericClassifierApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  114. def setLazyAnnotator(value: Boolean): GenericClassifierApproach.this.type
    Definition Classes
    CanBeLazy
  115. def setModelFile(modelFile: String): GenericClassifierApproach.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierParams
  116. def setMultiClass(value: Boolean): GenericClassifierApproach.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierParams
  117. final def setOutputCol(value: String): GenericClassifierApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  118. def setOutputLogsPath(outputLogsPath: String): GenericClassifierApproach.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierParams
  119. 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.

    Definition Classes
    GenericClassifierParams
  120. def setlearningRate(lr: Float): GenericClassifierApproach.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  121. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  122. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  123. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): GenericClassifierModel
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  124. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  125. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  126. val uid: String
    Definition Classes
    GenericClassifierApproach → Identifiable
  127. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  128. 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
  129. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  130. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  131. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  132. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

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