trait GenericClassifierParams extends Params

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Abstract Value Members

  1. abstract def copy(extra: ParamMap): Params
    Definition Classes
    Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
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  5. final def asInstanceOf[T0]: T0
    Definition Classes
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  6. val batchSize: IntParam

    Batch size

  7. final def clear(param: Param[_]): GenericClassifierParams.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
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    Annotations
    @throws( ... ) @native()
  9. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  10. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

  11. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. 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.

  13. val epochsN: IntParam

    Maximum number of epochs to train

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

    Feature scaling method.

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

  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  22. 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.

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

    Batch size

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

    get descriptive information about the dataset being used

  27. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. def getDropout: Float

    Dropout coefficient

  29. def getFeatureScaling: String

    Get feature scaling method

  30. def getFixImbalance: Boolean

    Fix imbalance in training set

  31. def getGraphFolder: String

    Folder with the TensorFlow graphs to choose the model graph from

  32. def getLabelColumn: String

    Column with label per each document

  33. def getLearningRate: Float

    Learning Rate

  34. def getMaxEpochs: Int

    Maximum number of epochs to train

  35. def getModelFile: String

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

  36. def getMultiClass: Boolean

    Gets the model multi class prediction mode

  37. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  38. def getOutputLogsPath: String

    Get output logs path

  39. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  40. 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.

  41. 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.

  42. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  43. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  44. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  46. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  47. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  48. val labelColumn: Param[String]

    Column with label per each document

  49. val learningRate: FloatParam

    Learning Rate

  50. 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.

  51. 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.

  52. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  53. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  54. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  55. 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).

  56. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  57. final def set(paramPair: ParamPair[_]): GenericClassifierParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  58. final def set(param: String, value: Any): GenericClassifierParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  59. final def set[T](param: Param[T], value: T): GenericClassifierParams.this.type
    Definition Classes
    Params
  60. def setBatchSize(batch: Int): GenericClassifierParams.this.type

    Batch size

  61. def setDatasetInfo(value: String): GenericClassifierParams.this.type

    set descriptive information about the dataset being used

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

    Dropout coefficient

  65. def setEpochsNumber(epochs: Int): GenericClassifierParams.this.type

    Maximum number of epochs to train

  66. def setFeatureScaling(featureScaling: String): GenericClassifierParams.this.type

    Set the feature scaling method.

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

  67. def setFixImbalance(fix: Boolean): GenericClassifierParams.this.type

    Fix imbalance of training set

  68. def setGraphFolder(path: String): GenericClassifierParams.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.

  69. def setLabelColumn(column: String): GenericClassifierParams.this.type

    Column with label per each document

  70. def setModelFile(modelFile: String): GenericClassifierParams.this.type

    Set the model file name

  71. def setMultiClass(value: Boolean): GenericClassifierParams.this.type

    Sets the model in multi class prediction mode

  72. def setOutputLogsPath(outputLogsPath: String): GenericClassifierParams.this.type

    Set the output log path

  73. def setValidationSplit(validationSplit: Float): GenericClassifierParams.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.

  74. def setlearningRate(lr: Float): GenericClassifierParams.this.type

    Learning Rate

  75. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  76. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  77. 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.

  78. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  79. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws( ... )
  80. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @throws( ... ) @native()

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