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
  • Generator

object Generator

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  9. def generateCorePyPrologue(sparkCompatVersion: String, out: PrintWriter): Unit
  10. def generateEnumDefinitions(enumNames: Set[String], out: PrintWriter): Unit
  11. def generateEstimatorAndModelWrapper[EstimatorClass, ModelClass](estimator: EstimatorClass, model: ModelClass, modelBaseClassName: String, estimatorDoc: String, modelDoc: String, sparkCompatVersion: String, out: PrintWriter)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[EstimatorClass], arg1: scala.reflect.api.JavaUniverse.TypeTag[ModelClass]): Unit
  12. def generateForwardedAccessors(accessors: Seq[(String, String)]): String
  13. def generateInitPy(modulePath: File, enumsUsedInParams: Set[String]): Unit
  14. def generateParamsDocStrings[ParamsClass](paramsClass: ParamsClass, tabShift: Int)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[ParamsClass]): String

    returns

    "param : type, default: <value> " <description>" ...

  15. def generateParamsGettersAndSetters[ParamsClass](paramsClass: ParamsClass)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[ParamsClass]): String
  16. def generateParamsInitialization(params: Params): String

    returns

    " self. = Param(...)" " self._setDefault(=)" ...

  17. def generateParamsKeywordArgs(params: Params): String

    returns

    "param=value, ..."

  18. def generateParamsPart[Params](params: Params, paramsKeywordArgs: String)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[Params]): String

    without init

  19. def generatePoolWrapper(out: PrintWriter): Unit
  20. def generateStandardParamsWrapper[ParamsClass](params: ParamsClass, out: PrintWriter)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[ParamsClass]): Unit
  21. def generateVersionPy(modulePath: File, version: String): Unit
  22. final def getClass(): Class[_]
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    @native()
  23. def getEnumNamesUsedInParams[Params](obj: Params)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[Params]): Set[String]
  24. def getParamNameToPythonTypeMap[Params](obj: Params)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[Params]): Map[String, String]
  25. def hashCode(): Int
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  26. final def isInstanceOf[T0]: Boolean
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  27. def jvmToPyValueAsString[T](obj: T): String
  28. def main(args: Array[String]): Unit

    args

    expects 3 arguments: 1) package version 2) output dir 3) spark compat version (like '2.4')

  29. final def ne(arg0: AnyRef): Boolean
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  30. final def notify(): Unit
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    @native()
  31. final def notifyAll(): Unit
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    @native()
  32. def patchJvmToPyEnumValue(enumValue: AnyRef): String
  33. final def synchronized[T0](arg0: ⇒ T0): T0
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  34. def toString(): String
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  35. final def wait(): Unit
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    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  37. final def wait(arg0: Long): Unit
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