class LightGBMRegressionModel extends RegressionModel[Vector, LightGBMRegressionModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with SynapseMLLogging

Model produced by LightGBMRegressor.

Linear Supertypes
SynapseMLLogging, ComplexParamsWritable, MLWritable, LightGBMPredictionParams, LightGBMModelMethods, LightGBMModelParams, Wrappable, RWrappable, PythonWrappable, BaseWrappable, RegressionModel[Vector, LightGBMRegressionModel], PredictionModel[Vector, LightGBMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[LightGBMRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LightGBMRegressionModel
  2. SynapseMLLogging
  3. ComplexParamsWritable
  4. MLWritable
  5. LightGBMPredictionParams
  6. LightGBMModelMethods
  7. LightGBMModelParams
  8. Wrappable
  9. RWrappable
  10. PythonWrappable
  11. BaseWrappable
  12. RegressionModel
  13. PredictionModel
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LightGBMRegressionModel()
  2. new LightGBMRegressionModel(uid: String)

Value Members

  1. final def clear(param: Param[_]): LightGBMRegressionModel.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): LightGBMRegressionModel
    Definition Classes
    LightGBMRegressionModel → Model → Transformer → PipelineStage → Params
  3. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  4. def explainParams(): String
    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  7. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  8. val featuresShapCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  9. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  10. def getBoosterBestIteration(): Int

    Public method to get the best iteration from the booster.

    Public method to get the best iteration from the booster.

    returns

    The best iteration, if early stopping was triggered.

    Definition Classes
    LightGBMModelMethods
  11. def getBoosterNumClasses(): Int

    Public method to get the number of classes from the booster.

    Public method to get the number of classes from the booster.

    returns

    The number of classes.

    Definition Classes
    LightGBMModelMethods
  12. def getBoosterNumFeatures(): Int

    Public method to get the number of features from the booster.

    Public method to get the number of features from the booster.

    returns

    The number of features.

    Definition Classes
    LightGBMModelMethods
  13. def getBoosterNumTotalIterations(): Int

    Public method to get the total number of iterations trained.

    Public method to get the total number of iterations trained.

    returns

    The total number of iterations trained.

    Definition Classes
    LightGBMModelMethods
  14. def getBoosterNumTotalModel(): Int

    Public method to get the total number of models trained.

    Public method to get the total number of models trained. Note this may be larger than the number of iterations, since in multiclass a model is trained per class for each iteration.

    returns

    The total number of models.

    Definition Classes
    LightGBMModelMethods
  15. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  16. def getDenseFeatureShaps(features: Array[Double]): Array[Double]

    Public method for pyspark API to get the dense local SHAP feature importance values for an instance.

    Public method for pyspark API to get the dense local SHAP feature importance values for an instance.

    features

    The local instance or row to compute the SHAP values for.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  17. def getFeatureImportances(importanceType: String): Array[Double]

    Public method to get the global feature importance values.

    Public method to get the global feature importance values.

    importanceType

    split or gini

    returns

    The global feature importance values.

    Definition Classes
    LightGBMModelMethods
  18. def getFeatureShaps(features: Vector): Array[Double]

    Public method to get the vector local SHAP feature importance values for an instance.

    Public method to get the vector local SHAP feature importance values for an instance.

    features

    The local instance or row to compute the SHAP values for.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  19. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  20. def getFeaturesShapCol: String
    Definition Classes
    LightGBMPredictionParams
  21. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  22. def getLeafPredictionCol: String
    Definition Classes
    LightGBMPredictionParams
  23. def getLightGBMBooster: LightGBMBooster
    Definition Classes
    LightGBMModelParams
  24. def getModel: LightGBMBooster

    Alias for same method

    Alias for same method

    returns

    The LightGBM Booster.

    Definition Classes
    LightGBMModelParams
  25. def getNativeModel(): String

    Gets the native model serialized representation as a string.

    Gets the native model serialized representation as a string.

    Definition Classes
    LightGBMModelMethods
  26. def getNumIterations: Int
    Definition Classes
    LightGBMModelParams
  27. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  28. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  29. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  30. def getPredictDisableShapeCheck: Boolean
    Definition Classes
    LightGBMPredictionParams
  31. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  32. def getSparseFeatureShaps(size: Int, indices: Array[Int], values: Array[Double]): Array[Double]

    Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.

    Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  33. def getStartIteration: Int
    Definition Classes
    LightGBMModelParams
  34. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  35. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  36. def hasParent: Boolean
    Definition Classes
    Model
  37. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  38. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  39. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  40. val leafPredictionCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  41. val lightGBMBooster: LightGBMBoosterParam
    Definition Classes
    LightGBMModelParams
  42. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  43. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  44. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  45. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  46. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  47. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  48. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  49. val numIterations: IntParam
    Definition Classes
    LightGBMModelParams
  50. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  51. var parent: Estimator[LightGBMRegressionModel]
    Definition Classes
    Model
  52. def predict(features: Vector): Double
    Definition Classes
    LightGBMRegressionModel → PredictionModel
  53. val predictDisableShapeCheck: BooleanParam
    Definition Classes
    LightGBMPredictionParams
  54. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  55. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  56. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  57. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  58. def saveNativeModel(filename: String, overwrite: Boolean): Unit

    Saves the native model serialized representation to file.

    Saves the native model serialized representation to file.

    filename

    The name of the file to save the model to

    overwrite

    Whether to overwrite if the file already exists

    Definition Classes
    LightGBMModelMethods
  59. final def set[T](param: Param[T], value: T): LightGBMRegressionModel.this.type
    Definition Classes
    Params
  60. def setFeaturesCol(value: String): LightGBMRegressionModel
    Definition Classes
    PredictionModel
  61. def setFeaturesShapCol(value: String): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  62. def setLeafPredictionCol(value: String): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  63. def setLightGBMBooster(value: LightGBMBooster): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  64. def setNumIterations(value: Int): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  65. def setParent(parent: Estimator[LightGBMRegressionModel]): LightGBMRegressionModel
    Definition Classes
    Model
  66. def setPredictDisableShapeCheck(value: Boolean): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  67. def setPredictionCol(value: String): LightGBMRegressionModel
    Definition Classes
    PredictionModel
  68. def setStartIteration(value: Int): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  69. val startIteration: IntParam
    Definition Classes
    LightGBMModelParams
  70. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  71. def transform(dataset: Dataset[_]): DataFrame

    Adds additional Leaf Index and SHAP columns if specified.

    Adds additional Leaf Index and SHAP columns if specified.

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    LightGBMRegressionModel → PredictionModel → Transformer
  72. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  73. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  74. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  75. val uid: String
    Definition Classes
    LightGBMRegressionModel → SynapseMLLogging → Identifiable
  76. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable