Packages

  • package root
    Definition Classes
    root
  • package ai
    Definition Classes
    root
  • package catboost
    Definition Classes
    ai
  • package spark

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    Overview

    This package provides classes that implement interfaces from Apache Spark Machine Learning Library (MLLib).

    For binary and multi- classification problems use CatBoostClassifier, for regression use CatBoostRegressor.

    These classes implement usual fit method of org.apache.spark.ml.Predictor that accept a single org.apache.spark.sql.DataFrame for training, but you can also use other fit method that accepts additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.

    This package also contains Pool class that is CatBoost's abstraction of a dataset. It contains additional information compared to simple org.apache.spark.sql.DataFrame.

    It is also possible to create Pool with quantized features before training by calling quantize method. This is useful if this dataset is used for training multiple times and quantization parameters do not change. Pre-quantized Pool allows to cache quantized features data and so do not re-run feature quantization step at the start of an each training.

    Detailed documentation is available on https://catboost.ai/docs/

    Definition Classes
    catboost
  • package impl
    Definition Classes
    spark
  • package pyspark_wrapper_generator
    Definition Classes
    impl
  • CtrFeatures
  • CtrsContext
  • ExpressionEncoderSerializer
  • FeatureImportanceCalcer
  • SerializationHelpers
c

ai.catboost.spark.impl

FeatureImportanceCalcer

class FeatureImportanceCalcer extends Logging

Linear Supertypes
Logging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. FeatureImportanceCalcer
  2. Logging
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new FeatureImportanceCalcer()

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def calc(model: TFullModel, fstrType: EFstrType, data: Pool = null, calcType: ECalcTypeShapValues = ECalcTypeShapValues.Regular): Array[Double]

    Supported values of fstrType are FeatureImportance, PredictionValuesChange, LossFunctionChange, PredictionDiff

    Supported values of fstrType are FeatureImportance, PredictionValuesChange, LossFunctionChange, PredictionDiff

    data

    if fstrType is PredictionDiff it is required and must contain 2 samples if fstrType is PredictionValuesChange this param is required in case if model was explicitly trained with flag to store no leaf weights. otherwise it can be null

    returns

    array of feature importances (index corresponds to order of features in the model)

  6. def calcInteraction(model: TFullModel): Array[FeatureInteractionScore]
  7. def calcLossFunctionChange(model: TFullModel, data: Pool, calcType: ECalcTypeShapValues): Array[Double]
  8. def calcPredictionDiff(model: TFullModel, data: Pool): Array[Double]
  9. def calcPredictionValuesChange(model: TFullModel, data: Pool): Array[Double]
  10. def calcShapInteractionValues(model: TFullModel, data: Pool, featureIndices: Pair[Int, Int], featureNames: Pair[String, String], preCalcMode: EPreCalcShapValues, calcType: ECalcTypeShapValues, outputColumns: Array[String]): DataFrame
  11. def calcShapValues(model: TFullModel, data: Pool, preCalcMode: EPreCalcShapValues, calcType: ECalcTypeShapValues, modelOutputType: EExplainableModelOutput, referenceData: Pool, outputColumns: Array[String]): DataFrame
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  16. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  19. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  22. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  23. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  30. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  34. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  35. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  36. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  37. def prepareTrees(model: TFullModel, data: Pool, preCalcMode: EPreCalcShapValues, calcInternalValues: Boolean, calcType: ECalcTypeShapValues, calcShapValuesByLeaf: Boolean, localExecutor: TLocalExecutor, modelOutputType: EExplainableModelOutput = EExplainableModelOutput.Raw, referenceData: Pool = null): TShapPreparedTrees
  38. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  41. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  42. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped