Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

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3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  def addValidationDataset(dataset: LightGBMDataset): Unit

   Adds the specified LightGBMDataset to be the validation dataset.

   Adds the specified LightGBMDataset to be the validation dataset.

   dataset

   The LightGBMDataset to add as the validation dataset.

5.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

6.  var bestIteration: Int
7.  lazy val boosterHandler: BoosterHandler

   Annotations

   @transient()

8.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

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   @throws( ... ) @native()

9.  def dumpModel(session: SparkSession, filename: String, overwrite: Boolean): Unit

   Dumps the native model pointer to file.

   Dumps the native model pointer to file.

   session

   The spark session

   filename

   The name of the file to save the model to

   overwrite

   Whether to overwrite if the file already exists

10.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

11.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

12.  def featuresShap(features: Vector): Array[Double]
13.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

14.  def freeNativeMemory(): Unit

    Frees any native memory held by the underlying booster pointer.

15.  final def getClass(): Class[_]

    Definition Classes

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    Annotations

    @native()

16.  def getEvalNames(): Array[String]

    Get the evaluation dataset column names from the native booster.

    Get the evaluation dataset column names from the native booster.

    returns

    The evaluation dataset column names.

17.  def getEvalResults(evalNames: Array[String], dataIndex: Int): Array[(String, Double)]

    Get the evaluation for the training data and validation data.

    Get the evaluation for the training data and validation data.

    evalNames

    The names of the evaluation metrics.

    dataIndex

    Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on.

    returns

    Array of tuples containing the evaluation metric name and metric value.

18.  def getFeatureImportances(importanceType: String): Array[Double]

    Calls into LightGBM to retrieve the feature importances.

    Calls into LightGBM to retrieve the feature importances.

    importanceType

    Can be "split" or "gain"

    returns

    The feature importance values as an array.

19.  def getNativeModel(): String

    Gets the native model serialized representation as a string.

20.  def hashCode(): Int

    Definition Classes

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    Annotations

    @native()

21.  def innerPredict(dataIndex: Int, classification: Boolean): Array[Array[Double]]

    Get predictions for the training and evaluation data on the booster.

    Get predictions for the training and evaluation data on the booster.

    dataIndex

    Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on.

    classification

    Whether this is a classification scenario or not.

    returns

    The predictions as a 2D array where first level is for row index and second level is optional if there are classes.

22.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

23.  def mergeBooster(model: String): Unit

    Merges this Booster with the specified model.

    Merges this Booster with the specified model.

    model

    The string serialized representation of the learner to merge.

24.  val modelStr: Option[String]
25.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

26.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

27.  final def notifyAll(): Unit

    Definition Classes

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    @native()

28.  lazy val numClasses: Int
29.  lazy val numFeatures: Int
30.  lazy val numModelPerIteration: Int
31.  lazy val numTotalIterations: Int
32.  lazy val numTotalModel: Int
33.  val parameters: Option[String]
34.  def predictForCSR(sparseVector: SparseVector, kind: Int, disableShapeCheck: Boolean, dataLengthLongPtr: SWIGTYPE_p_long_long, dataOutPtr: SWIGTYPE_p_double): Unit

    Attributes

    protected

35.  def predictForMat(row: Array[Double], kind: Int, disableShapeCheck: Boolean, dataLengthLongPtr: SWIGTYPE_p_long_long, dataOutPtr: SWIGTYPE_p_double): Unit

    Attributes

    protected

36.  def predictLeaf(features: Vector): Array[Double]
37.  def resetParameter(newParameters: String): Unit

    Reset the specified parameters on the native booster.

    Reset the specified parameters on the native booster.

    newParameters

    The new parameters to set.

38.  def saveNativeModel(session: SparkSession, filename: String, overwrite: Boolean): Unit

    Saves the native model serialized representation to file.

    Saves the native model serialized representation to file.

    session

    The spark session

    filename

    The name of the file to save the model to

    overwrite

    Whether to overwrite if the file already exists

39.  def saveToString(upToIteration: Option[Int] = None): String

    Saves the booster to string representation.

    Saves the booster to string representation.

    upToIteration

    The zero-based index of the iteration to save as the last one (ignoring the rest).

    returns

    The serialized string representation of the Booster.

40.  def score(features: Vector, raw: Boolean, classification: Boolean, disableShapeCheck: Boolean): Array[Double]
41.  def setBestIteration(bestIteration: Int): Unit

    Sets the best iteration and also the numIterations to be the best iteration.

    Sets the best iteration and also the numIterations to be the best iteration.

    bestIteration

    The best iteration computed by early stopping.

42.  def setNumIterations(numIterations: Int): Unit

    Sets the total number of iterations used in the prediction.

    Sets the total number of iterations used in the prediction. If <= 0, all iterations from start_iteration are used (no limits).

    numIterations

    The total number of iterations used in the prediction.

43.  def setStartIteration(startIteration: Int): Unit

    Sets the start index of the iteration to predict.

    Sets the start index of the iteration to predict. If <= 0, starts from the first iteration.

    startIteration

    The start index of the iteration to predict.

44.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

45.  def toString(): String

    Definition Classes

    AnyRef → Any

46.  val trainDataset: Option[LightGBMDataset]
47.  def updateOneIteration(): Boolean

    Updates the booster for one iteration.

    Updates the booster for one iteration.

    returns

    True if terminated training early.

48.  def updateOneIterationCustom(gradient: Array[Float], hessian: Array[Float]): Boolean

    Updates the booster with custom loss function for one iteration.

    Updates the booster with custom loss function for one iteration.

    gradient

    The gradient from custom loss function.

    hessian

    The hessian matrix from custom loss function.

    returns

    True if terminated training early.

49.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

50.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

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    Annotations

    @throws( ... )

51.  final def wait(arg0: Long): Unit

    Definition Classes

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    @throws( ... ) @native()