trait LightGBMModelMethods extends LightGBMModelParams with Logging
Contains common LightGBM model methods across all LightGBM learner types.
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lazy val
classNameHelper: String
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def
clear(param: Param[_]): LightGBMModelMethods.this.type
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clone(): AnyRef
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def
companionModelClassName: String
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copyValues[T <: Params](to: T, extra: ParamMap): T
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defaultCopy[T <: Params](extra: ParamMap): T
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explainParam(param: Param[_]): String
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def
extractParamMap(): ParamMap
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def
extractParamMap(extra: ParamMap): ParamMap
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def
featuresShap(features: Vector): Vector
Protected method to predict local SHAP feature importance values for an instance.
Protected method to predict local SHAP feature importance values for an instance.
- features
The local instance or row to compute the local SHAP values for.
- returns
The SHAP local feature importance values.
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finalize(): Unit
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def
get[T](param: Param[T]): Option[T]
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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.
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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.
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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.
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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.
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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.
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final
def
getClass(): Class[_]
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def
getDefault[T](param: Param[T]): Option[T]
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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.
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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.
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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.
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def
getLightGBMBooster: LightGBMBooster
- Definition Classes
- LightGBMModelParams
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def
getModel: LightGBMBooster
Alias for same method
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def
getNumIterations: Int
- Definition Classes
- LightGBMModelParams
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final
def
getOrDefault[T](param: Param[T]): T
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def
getParam(paramName: String): Param[Any]
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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.
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def
getStartIteration: Int
- Definition Classes
- LightGBMModelParams
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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def
isTraceEnabled(): Boolean
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val
lightGBMBooster: LightGBMBoosterParam
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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logWarning(msg: ⇒ String): Unit
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def
notifyAll(): Unit
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val
numIterations: IntParam
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- LightGBMModelParams
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lazy val
params: Array[Param[_]]
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def
predictLeaf(features: Vector): Vector
Protected method to predict leaf index.
Protected method to predict leaf index.
- features
The local instance or row to compute the leaf index for.
- returns
The predicted leaf index.
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def
pyAdditionalMethods: String
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pyClassDoc: String
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pyClassName: String
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pyExtraEstimatorImports: String
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pyExtraEstimatorMethods: String
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pyInheritedClasses: Seq[String]
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def
pyInitFunc(): String
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lazy val
pyInternalWrapper: Boolean
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pyObjectBaseClass: String
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def
pyParamArg[T](p: Param[T]): String
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pyParamsArgs: String
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pyParamsDefaults: String
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pyParamsDefinitions: String
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pyParamsGetters: String
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rExtraInitLines: String
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rFuncName: String
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rInternalWrapper: Boolean
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def
set(paramPair: ParamPair[_]): LightGBMModelMethods.this.type
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def
set(param: String, value: Any): LightGBMModelMethods.this.type
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def
set[T](param: Param[T], value: T): LightGBMModelMethods.this.type
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def
setDefault(paramPairs: ParamPair[_]*): LightGBMModelMethods.this.type
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def
setDefault[T](param: Param[T], value: T): LightGBMModelMethods.this.type
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def
setLightGBMBooster(value: LightGBMBooster): LightGBMModelMethods.this.type
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def
setNumIterations(value: Int): LightGBMModelMethods.this.type
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def
setStartIteration(value: Int): LightGBMModelMethods.this.type
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val
startIteration: IntParam
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updateBoosterParamsBeforePredict(): Unit
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