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com.johnsnowlabs.nlp.annotators.param

EvaluationDLParams

trait EvaluationDLParams extends Params

Linear Supertypes
Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
ClassifierDLApproach, ClassifierEncoder, MultiClassifierDLApproach, SentimentDLApproach, NerDLApproach
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Inherited
  1. EvaluationDLParams
  2. Params
  3. Serializable
  4. Serializable
  5. Identifiable
  6. AnyRef
  7. Any
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Visibility
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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. final def clear(param: Param[_]): EvaluationDLParams.this.type
    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  9. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  10. val enableOutputLogs: BooleanParam

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

  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. val evaluationLogExtended: BooleanParam

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

  14. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  15. def explainParams(): String
    Definition Classes
    Params
  16. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  17. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  18. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  22. def getEnableOutputLogs: Boolean

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

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

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

  25. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  26. 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.

  27. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  28. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  29. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  31. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  32. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  33. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  34. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  35. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  36. val outputLogsPath: Param[String]

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

  37. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  38. final def set(paramPair: ParamPair[_]): EvaluationDLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  39. final def set(param: String, value: Any): EvaluationDLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  40. final def set[T](param: Param[T], value: T): EvaluationDLParams.this.type
    Definition Classes
    Params
  41. final def setDefault(paramPairs: ParamPair[_]*): EvaluationDLParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  42. final def setDefault[T](param: Param[T], value: T): EvaluationDLParams.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  43. def setEnableOutputLogs(enableOutputLogs: Boolean): EvaluationDLParams.this.type

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

  44. def setEvaluationLogExtended(evaluationLogExtended: Boolean): EvaluationDLParams.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.

  45. def setOutputLogsPath(path: String): EvaluationDLParams.this.type

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

  46. def setTestDataset(er: ExternalResource): EvaluationDLParams.this.type

    ExternalResource to a parquet file of a test dataset.

    ExternalResource to a parquet file of a test dataset. If set, it is used to calculate statistics on it during training.

    When using an ExternalResource, only parquet files are accepted for this function.

    The parquet file must be a dataframe that has the same columns as the model that is being trained. For example, if the model needs as input DOCUMENT, TOKEN, WORD_EMBEDDINGS (Features) and NAMED_ENTITY (label) then these columns also need to be present while saving the dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe.

    An example on how to create such a parquet file could be:

    // assuming preProcessingPipeline
val Array(train, test) = data.randomSplit(Array(0.8, 0.2))

preProcessingPipeline
  .fit(test)
  .transform(test)
  .write
  .mode("overwrite")
  .parquet("test_data")

annotator.setTestDataset("test_data")
  47. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): EvaluationDLParams.this.type

    Path to a parquet file of a test dataset.

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

    The parquet file must be a dataframe that has the same columns as the model that is being trained. For example, if the model needs as input DOCUMENT, TOKEN, WORD_EMBEDDINGS (Features) and NAMED_ENTITY (label) then these columns also need to be present while saving the dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe.

    An example on how to create such a parquet file could be:

    // assuming preProcessingPipeline
val Array(train, test) = data.randomSplit(Array(0.8, 0.2))

preProcessingPipeline
  .fit(test)
  .transform(test)
  .write
  .mode("overwrite")
  .parquet("test_data")

annotator.setTestDataset("test_data")
  48. def setValidationSplit(validationSplit: Float): EvaluationDLParams.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.

  49. def setVerbose(verbose: Level): EvaluationDLParams.this.type

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

  50. def setVerbose(verbose: Int): EvaluationDLParams.this.type

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

  51. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  52. val testDataset: ExternalResourceParam

    Path to a parquet file of a test dataset.

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

  53. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  54. 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.

  55. val verbose: IntParam

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

  56. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  57. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  58. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

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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