com.microsoft.azure.synapse.ml.lightgbm
LightGBMRankerModel
Companion object LightGBMRankerModel
class LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with SynapseMLLogging
Model produced by LightGBMRanker.
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- LightGBMRankerModel
- SynapseMLLogging
- ComplexParamsWritable
- MLWritable
- LightGBMPredictionParams
- LightGBMModelMethods
- LightGBMModelParams
- Wrappable
- DotnetWrappable
- RWrappable
- PythonWrappable
- BaseWrappable
- RankerModel
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
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lazy val
classNameHelper: String
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final
def
clear(param: Param[_]): LightGBMRankerModel.this.type
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def
clone(): AnyRef
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def
companionModelClassName: String
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def
copy(extra: ParamMap): LightGBMRankerModel
- Definition Classes
- LightGBMRankerModel → Model → Transformer → PipelineStage → Params
-
def
copyValues[T <: Params](to: T, extra: ParamMap): T
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lazy val
copyrightLines: String
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def
defaultCopy[T <: Params](extra: ParamMap): T
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dotnetAdditionalMethods: String
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dotnetClass(): String
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lazy val
dotnetClassName: String
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dotnetClassNameString: String
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dotnetClassWrapperName: String
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dotnetExtraEstimatorImports: String
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dotnetExtraMethods: String
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dotnetInternalWrapper: Boolean
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def
dotnetMLReadWriteMethods: String
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dotnetNamespace: String
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dotnetObjectBaseClass: String
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dotnetParamGetter(p: Param[_]): String
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dotnetParamGetters: String
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dotnetParamSetter(p: Param[_]): String
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dotnetParamSetters: String
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dotnetWrapAsTypeMethod: String
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
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def
extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
- Attributes
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- PredictorParams
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def
extractInstances(dataset: Dataset[_]): RDD[Instance]
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final
def
extractParamMap(): ParamMap
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
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final
val
featuresCol: Param[String]
- Definition Classes
- HasFeaturesCol
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def
featuresDataType: DataType
- Attributes
- protected
- Definition Classes
- PredictionModel
-
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.
- Attributes
- protected
- Definition Classes
- LightGBMModelMethods
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val
featuresShapCol: Param[String]
- Definition Classes
- LightGBMPredictionParams
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def
finalize(): Unit
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final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
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
-
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
-
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
-
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
-
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
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
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
-
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
-
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
-
final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
def
getFeaturesShapCol: String
- Definition Classes
- LightGBMPredictionParams
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final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
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def
getLeafPredictionCol: String
- Definition Classes
- LightGBMPredictionParams
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def
getLightGBMBooster: LightGBMBooster
- Definition Classes
- LightGBMModelParams
-
def
getModel: LightGBMBooster
Alias for same method
-
def
getNativeModel(): String
Gets the native model serialized representation as a string.
Gets the native model serialized representation as a string.
- Definition Classes
- LightGBMModelMethods
-
def
getNumIterations: Int
- Definition Classes
- LightGBMModelParams
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
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def
getParamInfo(p: Param[_]): ParamInfo[_]
- Definition Classes
- BaseWrappable
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def
getPayload(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], exception: Option[Exception]): Map[String, String]
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
getPredictDisableShapeCheck: Boolean
- Definition Classes
- LightGBMPredictionParams
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final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
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
-
def
getStartIteration: Int
- Definition Classes
- LightGBMModelParams
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
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def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
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def
hasParent: Boolean
- Definition Classes
- Model
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
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final
def
isDefined(param: Param[_]): Boolean
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final
def
isInstanceOf[T0]: Boolean
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- Any
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final
def
isSet(param: Param[_]): Boolean
- Definition Classes
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def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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final
val
labelCol: Param[String]
- Definition Classes
- HasLabelCol
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val
leafPredictionCol: Param[String]
- Definition Classes
- LightGBMPredictionParams
-
val
lightGBMBooster: LightGBMBoosterParam
- Definition Classes
- LightGBMModelParams
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logBase(info: Map[String, String], featureName: Option[String]): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logBase(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], featureName: Option[String]): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logClass(featureName: String): Unit
- Definition Classes
- SynapseMLLogging
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String): Unit
- Attributes
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- Logging
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
- Logging
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def
logError(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
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def
logErrorBase(methodName: String, e: Exception): Unit
- Attributes
- protected
- Definition Classes
- SynapseMLLogging
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def
logFit[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logTransform[T](f: ⇒ T, columns: Int): T
- Definition Classes
- SynapseMLLogging
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def
logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
- Definition Classes
- SynapseMLLogging
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
-
def
makeDotnetFile(conf: CodegenConfig): Unit
- Definition Classes
- DotnetWrappable
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def
makePyFile(conf: CodegenConfig): Unit
- Definition Classes
- PythonWrappable
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def
makeRFile(conf: CodegenConfig): Unit
- Definition Classes
- RWrappable
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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- @native()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
numFeatures: Int
- Definition Classes
- LightGBMRankerModel → PredictionModel
-
val
numIterations: IntParam
- Definition Classes
- LightGBMModelParams
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[LightGBMRankerModel]
- Definition Classes
- Model
-
def
predict(features: Vector): Double
- Definition Classes
- LightGBMRankerModel → PredictionModel
-
val
predictDisableShapeCheck: BooleanParam
- Definition Classes
- LightGBMPredictionParams
-
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.
- Attributes
- protected
- Definition Classes
- LightGBMModelMethods
-
final
val
predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
-
def
pyAdditionalMethods: String
- Definition Classes
- PythonWrappable
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lazy val
pyClassDoc: String
- Attributes
- protected
- Definition Classes
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lazy val
pyClassName: String
- Attributes
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- Definition Classes
- PythonWrappable
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def
pyExtraEstimatorImports: String
- Attributes
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- Definition Classes
- PythonWrappable
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def
pyExtraEstimatorMethods: String
- Attributes
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- Definition Classes
- PythonWrappable
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lazy val
pyInheritedClasses: Seq[String]
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyInitFunc(): String
- Definition Classes
- PythonWrappable
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lazy val
pyInternalWrapper: Boolean
- Attributes
- protected
- Definition Classes
- LightGBMRankerModel → PythonWrappable
-
lazy val
pyObjectBaseClass: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamArg[T](p: Param[T]): String
- Attributes
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- Definition Classes
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def
pyParamDefault[T](p: Param[T]): Option[String]
- Attributes
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def
pyParamGetter(p: Param[_]): String
- Attributes
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def
pyParamSetter(p: Param[_]): String
- Attributes
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- Definition Classes
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def
pyParamsArgs: String
- Attributes
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- Definition Classes
- PythonWrappable
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def
pyParamsDefaults: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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lazy val
pyParamsDefinitions: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamsGetters: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pyParamsSetters: String
- Attributes
- protected
- Definition Classes
- PythonWrappable
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def
pythonClass(): String
- Attributes
- protected
- Definition Classes
- PythonWrappable
-
def
rClass(): String
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rDocString: String
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rExtraBodyLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rExtraInitLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
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lazy val
rFuncName: String
- Attributes
- protected
- Definition Classes
- RWrappable
-
lazy val
rInternalWrapper: Boolean
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rParamArg[T](p: Param[T]): String
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
rParamsArgs: String
- Attributes
- protected
- Definition Classes
- RWrappable
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def
rSetterLines: String
- Attributes
- protected
- Definition Classes
- RWrappable
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
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
-
final
def
set(paramPair: ParamPair[_]): LightGBMRankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): LightGBMRankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): LightGBMRankerModel.this.type
- Definition Classes
- Params
-
final
def
setDefault(paramPairs: ParamPair[_]*): LightGBMRankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): LightGBMRankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): LightGBMRankerModel
- Definition Classes
- PredictionModel
-
def
setFeaturesShapCol(value: String): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setLeafPredictionCol(value: String): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setLightGBMBooster(value: LightGBMBooster): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
def
setNumIterations(value: Int): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
def
setParent(parent: Estimator[LightGBMRankerModel]): LightGBMRankerModel
- Definition Classes
- Model
-
def
setPredictDisableShapeCheck(value: Boolean): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMPredictionParams
-
def
setPredictionCol(value: String): LightGBMRankerModel
- Definition Classes
- PredictionModel
-
def
setStartIteration(value: Int): LightGBMRankerModel.this.type
- Definition Classes
- LightGBMModelParams
-
val
startIteration: IntParam
- Definition Classes
- LightGBMModelParams
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
val
thisStage: Params
- Attributes
- protected
- Definition Classes
- BaseWrappable
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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
- LightGBMRankerModel → PredictionModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformImpl(dataset: Dataset[_]): DataFrame
- Attributes
- protected
- Definition Classes
- PredictionModel
-
def
transformSchema(schema: StructType): StructType
- Definition Classes
- PredictionModel → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- LightGBMRankerModel → SynapseMLLogging → Identifiable
-
def
updateBoosterParamsBeforePredict(): Unit
- Attributes
- protected
- Definition Classes
- LightGBMModelMethods
-
def
validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- PredictorParams
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
write: MLWriter
- Definition Classes
- ComplexParamsWritable → MLWritable