Packages

trait ClassifierEncoder extends EvaluationDLParams

Linear Supertypes
EvaluationDLParams, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
ClassifierDLApproach, MultiClassifierDLApproach, SentimentDLApproach
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Inherited
  1. ClassifierEncoder
  2. EvaluationDLParams
  3. Params
  4. Serializable
  5. Serializable
  6. Identifiable
  7. AnyRef
  8. Any
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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
    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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val batchSize: IntParam

    Batch size (Default: 64)

  7. def buildDatasetWithLabels(dataset: Dataset[_], inputCols: String): (DataFrame, Array[String])
    Attributes
    protected
  8. final def clear(param: Param[_]): ClassifierEncoder.this.type
    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  10. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  11. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  13. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder (Default: false)

    Whether to output to annotators log folder (Default: false)

    Definition Classes
    EvaluationDLParams
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. val evaluationLogExtended: BooleanParam

    Whether logs for validation to be extended (Default: false): it displays time and evaluation of each label

    Whether logs for validation to be extended (Default: false): it displays time and evaluation of each label

    Definition Classes
    EvaluationDLParams
  17. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  18. def explainParams(): String
    Definition Classes
    Params
  19. def extractInputs(encoder: ClassifierDatasetEncoder, dataframe: DataFrame): (Array[Array[Float]], Array[String])
    Attributes
    protected
  20. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  22. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  24. def getBatchSize: Int

    Batch size (Default: 64)

  25. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  26. def getConfigProtoBytes: Option[Array[Byte]]

    Tensorflow config Protobytes passed to the TF session

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

    Whether to output to annotators log folder (Default: false)

    Whether to output to annotators log folder (Default: false)

    Definition Classes
    EvaluationDLParams
  29. def getLabelColumn: String

    Column with label per each document

  30. def getLr: Float

    Learning Rate (Default: 5e-3f)

  31. def getMaxEpochs: Int

    Maximum number of epochs to train (Default: 10)

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

    Folder path to save training logs (Default: "")

    Folder path to save training logs (Default: "")

    Definition Classes
    EvaluationDLParams
  34. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  35. def getRandomSeed: Int

    Random seed

  36. def getValidationSplit: Float

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    EvaluationDLParams
  37. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  38. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  39. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  40. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  41. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  42. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  43. val labelColumn: Param[String]

    Column with label per each document

  44. val lr: FloatParam

    Learning Rate (Default: 5e-3f)

  45. val maxEpochs: IntParam

    Maximum number of epochs to train (Default: 10)

  46. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  47. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  48. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  49. val outputLogsPath: Param[String]

    Folder path to save training logs (Default: "")

    Folder path to save training logs (Default: "")

    Definition Classes
    EvaluationDLParams
  50. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  51. val randomSeed: IntParam

    Random seed for shuffling the dataset

  52. final def set(paramPair: ParamPair[_]): ClassifierEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  53. final def set(param: String, value: Any): ClassifierEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  54. final def set[T](param: Param[T], value: T): ClassifierEncoder.this.type
    Definition Classes
    Params
  55. def setBatchSize(batch: Int): ClassifierEncoder.this.type

    Batch size (Default: 64)

  56. def setConfigProtoBytes(bytes: Array[Int]): ClassifierEncoder.this.type

    Tensorflow config Protobytes passed to the TF session

  57. final def setDefault(paramPairs: ParamPair[_]*): ClassifierEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  58. final def setDefault[T](param: Param[T], value: T): ClassifierEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  59. def setEnableOutputLogs(enableOutputLogs: Boolean): ClassifierEncoder.this.type

    Whether to output to annotators log folder (Default: false)

    Whether to output to annotators log folder (Default: false)

    Definition Classes
    EvaluationDLParams
  60. def setEvaluationLogExtended(evaluationLogExtended: Boolean): ClassifierEncoder.this.type

    Whether logs for validation to be extended: it displays time and evaluation of each label.

    Whether logs for validation to be extended: it displays time and evaluation of each label. Default is false.

    Definition Classes
    EvaluationDLParams
  61. def setLabelColumn(column: String): ClassifierEncoder.this.type

    Column with label per each document

  62. def setLr(lr: Float): ClassifierEncoder.this.type

    Learning Rate (Default: 5e-3f)

  63. def setMaxEpochs(epochs: Int): ClassifierEncoder.this.type

    Maximum number of epochs to train (Default: 10)

  64. def setOutputLogsPath(path: String): ClassifierEncoder.this.type

    Folder path to save training logs (Default: "")

    Folder path to save training logs (Default: "")

    Definition Classes
    EvaluationDLParams
  65. def setRandomSeed(seed: Int): ClassifierEncoder.this.type

    Random seed

  66. def setTestDataset(er: ExternalResource): ClassifierEncoder.this.type

    Path to test dataset.

    Path to test dataset. If set, it is used to calculate statistics on it during training.

    Definition Classes
    EvaluationDLParams
  67. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): ClassifierEncoder.this.type

    Path to test dataset.

    Path to test dataset. If set, it is used to calculate statistics on it during training.

    Definition Classes
    EvaluationDLParams
  68. def setValidationSplit(validationSplit: Float): ClassifierEncoder.this.type

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    EvaluationDLParams
  69. def setVerbose(verbose: Level): ClassifierEncoder.this.type

    Level of verbosity during training (Default: Verbose.Silent.id)

    Level of verbosity during training (Default: Verbose.Silent.id)

    Definition Classes
    EvaluationDLParams
  70. def setVerbose(verbose: Int): ClassifierEncoder.this.type

    Level of verbosity during training (Default: Verbose.Silent.id)

    Level of verbosity during training (Default: Verbose.Silent.id)

    Definition Classes
    EvaluationDLParams
  71. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  72. val testDataset: ExternalResourceParam

    Path to test dataset.

    Path to test dataset. If set, it is used to calculate statistics on it during training.

    Definition Classes
    EvaluationDLParams
  73. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  74. val validationSplit: FloatParam

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    EvaluationDLParams
  75. val verbose: IntParam

    Level of verbosity during training (Default: Verbose.Silent.id)

    Level of verbosity during training (Default: Verbose.Silent.id)

    Definition Classes
    EvaluationDLParams
  76. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  77. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  78. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from EvaluationDLParams

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

setParam

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