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  • package root
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    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
o

ai.catboost.spark.impl

CtrFeatures

object CtrFeatures

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  4. def addCtrProviderToModel(model: TFullModel, ctrsContext: CtrsContext, quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool]): TFullModel
  5. def addCtrsAsEstimated(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], updatedCatBoostJsonParams: JObject, oneHotMaxSize: Int, classTargetPreprocessor: Option[TClassTargetPreprocessor], serializedLabelConverter: TVector_i8): (Pool, Array[Pool], CtrsContext)

    returns

    (trainPoolWithEstimatedFeatures, evalPoolsWithEstimatedFeatures, ctrsContext)

    Note

    if repartitioning is applied to result pools CtrsContext will no longer work with them

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  8. def downloadSubsetOfQuantizedFeatures(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], quantizedFeaturesIndices: QuantizedFeaturesIndices, selectedFlatFeaturesIndices: Set[Int], localExecutor: TLocalExecutor): (TQuantizedObjectsDataProviderPtr, TVector_TQuantizedObjectsDataProviderPtr)
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  12. def getCatFeatureFlatIndicesForCtrs(pool: Pool, oneHotMaxSize: Int): Array[Int]

    returns

    array of flat feature indices

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  14. def getDatasetWithIdsAndIds(df: DataFrame): (DataFrame, Array[Long])
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  15. def getPreprocessedLearnTarget(pool: Pool, classTargetPreprocessor: Option[TClassTargetPreprocessor]): TVector_float
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  23. def uploadAndMerge(spark: SparkSession, schema: StructType, aggregateData: DataFrame, ids: Array[Long], estimatedData: TQuantizedObjectsDataProviderPtr): DataFrame
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