class CatBoostRegressor extends CatBoostRegressorBase[Vector, CatBoostRegressor, CatBoostRegressionModel] with CatBoostPredictorTrait[CatBoostRegressor, CatBoostRegressionModel] with RegressorTrainingParamsTrait
Class to train CatBoostRegressionModel
The default optimized loss function is RMSE
Examples
Basic example.
val spark = SparkSession.builder() .master("local[*]") .appName("RegressorTest") .getOrCreate(); val srcDataSchema = Seq( StructField("features", SQLDataTypes.VectorType), StructField("label", StringType) ) val trainData = Seq( Row(Vectors.dense(0.1, 0.2, 0.11), "0.12"), Row(Vectors.dense(0.97, 0.82, 0.33), "0.22"), Row(Vectors.dense(0.13, 0.22, 0.23), "0.34"), Row(Vectors.dense(0.8, 0.62, 0.0), "0.1") ) val trainDf = spark.createDataFrame(spark.sparkContext.parallelize(trainData), StructType(srcDataSchema)) val trainPool = new Pool(trainDf) val evalData = Seq( Row(Vectors.dense(0.22, 0.33, 0.9), "0.1"), Row(Vectors.dense(0.11, 0.1, 0.21), "0.9"), Row(Vectors.dense(0.77, 0.0, 0.0), "0.72") ) val evalDf = spark.createDataFrame(spark.sparkContext.parallelize(evalData), StructType(srcDataSchema)) val evalPool = new Pool(evalDf) val regressor = new CatBoostRegressor val model = regressor.fit(trainPool, Array[Pool](evalPool)) val predictions = model.transform(evalPool.data) predictions.show()
Example with alternative loss function.
...<initialize trainPool, evalPool> val regressor = new CatBoostRegressor().setLossFunction("MAE") val model = regressor.fit(trainPool, Array[Pool](evalPool)) val predictions = model.transform(evalPool.data) predictions.show()
Serialization
Supports standard Spark MLLib serialization. Data can be saved to distributed filesystem like HDFS or local files.
Examples:
Save:
val regressor = new CatBoostRegressor().setLossFunction("MAE") val path = "/home/user/catboost_regressors/regressor0" regressor.write.save(path)
Load:
val path = "/home/user/catboost_regressors/regressor0" val regressor = CatBoostRegressor.load(path) val trainPool : Pool = ... init Pool ... val model = regressor.fit(trainPool)
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- CatBoostRegressor
- RegressorTrainingParamsTrait
- TrainingParamsTrait
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- def addEstimatedCtrFeatures(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], updatedCatBoostJsonParams: JObject, classTargetPreprocessor: Option[TClassTargetPreprocessor] = None, serializedLabelConverter: TVector_i8 = new TVector_i8): (Pool, Array[Pool], CtrsContext)
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(preprocessedTrainPool, preprocessedEvalPools, ctrsContext)
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- final val allowConstLabel: BooleanParam
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- final val bootstrapType: EnumParam[EBootstrapType]
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- def copy(extra: ParamMap): CatBoostRegressor
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- CatBoostRegressor → Predictor → Estimator → PipelineStage → Params
- def copyValues[T <: Params](to: T, extra: ParamMap): T
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- def createModel(nativeModel: TFullModel): CatBoostRegressionModel
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- def fit(trainPool: Pool, evalPools: Array[Pool] = Array[Pool]()): CatBoostRegressionModel
Additional variant of
fitmethod that accepts CatBoost's Pool s and allows to specify additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.Additional variant of
fitmethod that accepts CatBoost's Pool s and allows to specify additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.- trainPool
The input training dataset.
- evalPools
The validation datasets used for the following processes:
- overfitting detector
- best iteration selection
- monitoring metrics' changes
- returns
trained model
- Definition Classes
- CatBoostPredictorTrait
- def fit(dataset: Dataset[_]): CatBoostRegressionModel
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- def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[CatBoostRegressionModel]
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- def fit(dataset: Dataset[_], paramMap: ParamMap): CatBoostRegressionModel
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- def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): CatBoostRegressionModel
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- final val foldLenMultiplier: FloatParam
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- final def getFeatureBorderType: EBorderSelectionType
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- final def getFeatureWeightsList: Array[Double]
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- final def getL2LeafReg: Float
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- final def getOdPval: Float
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- final def getPerObjectFeaturePenaltiesList: Array[Double]
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- final def getPerObjectFeaturePenaltiesMap: LinkedHashMap[String, Double]
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- final def getPredictionCol: String
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- final def getRandomSeed: Int
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- final def getRandomStrength: Float
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- final def getRsm: Float
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- final def getSamplingFrequency: ESamplingFrequency
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- final def getScoreFunction: EScoreFunction
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- final val predictionCol: Param[String]
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- HasPredictionCol
- def preprocessBeforeTraining(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool]): (Pool, Array[Pool], CatBoostTrainingContext)
override in descendants if necessary
override in descendants if necessary
- returns
(preprocessedTrainPool, preprocessedEvalPools, catBoostTrainingContext)
- Attributes
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- CatBoostPredictorTrait
- final val randomSeed: IntParam
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- final val randomStrength: FloatParam
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- final val rsm: FloatParam
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- final val saveSnapshot: BooleanParam
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- final val scoreFunction: EnumParam[EScoreFunction]
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- final def set(paramPair: ParamPair[_]): CatBoostRegressor.this.type
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- final def setAllowConstLabel(value: Boolean): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setAllowWritingFiles(value: Boolean): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setApproxOnFullHistory(value: Boolean): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setBaggingTemperature(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setBestModelMinTrees(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setBootstrapType(value: EBootstrapType): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setBorderCount(value: Int): CatBoostRegressor.this.type
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- QuantizationParamsTrait
- final def setConnectTimeout(value: Duration): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setCustomMetric(value: Array[String]): CatBoostRegressor.this.type
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- TrainingParamsTrait
- final def setDefault(paramPairs: ParamPair[_]*): CatBoostRegressor.this.type
- Attributes
- protected
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- Params
- final def setDefault[T](param: Param[T], value: T): CatBoostRegressor.this.type
- Attributes
- protected[ml]
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- Params
- final def setDepth(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setDiffusionTemperature(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setEarlyStoppingRounds(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setEvalMetric(value: String): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setFeatureBorderType(value: EBorderSelectionType): CatBoostRegressor.this.type
- Definition Classes
- QuantizationParamsTrait
- final def setFeatureWeightsList(value: Array[Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setFeatureWeightsMap(value: LinkedHashMap[String, Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- def setFeaturesCol(value: String): CatBoostRegressor
- Definition Classes
- Predictor
- final def setFirstFeatureUsePenaltiesList(value: Array[Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setFirstFeatureUsePenaltiesMap(value: LinkedHashMap[String, Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setFoldLenMultiplier(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setFoldPermutationBlock(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setHasTime(value: Boolean): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setIgnoredFeaturesIndices(value: Array[Int]): CatBoostRegressor.this.type
- Definition Classes
- IgnoredFeaturesParams
- final def setIgnoredFeaturesNames(value: Array[String]): CatBoostRegressor.this.type
- Definition Classes
- IgnoredFeaturesParams
- final def setInputBorders(value: String): CatBoostRegressor.this.type
- Definition Classes
- QuantizationParamsTrait
- final def setIterations(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setL2LeafReg(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- def setLabelCol(value: String): CatBoostRegressor
- Definition Classes
- Predictor
- final def setLeafEstimationBacktracking(value: ELeavesEstimationStepBacktracking): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setLeafEstimationIterations(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setLeafEstimationMethod(value: ELeavesEstimation): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setLearningRate(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setLoggingLevel(value: ELoggingLevel): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setLossFunction(value: String): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setMetricPeriod(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setModelShrinkMode(value: EModelShrinkMode): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setModelShrinkRate(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setMvsReg(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setNanMode(value: ENanMode): CatBoostRegressor.this.type
- Definition Classes
- QuantizationParamsTrait
- final def setOdPval(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setOdType(value: EOverfittingDetectorType): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setOdWait(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setOneHotMaxSize(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setPenaltiesCoefficient(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setPerFloatFeatureQuantizaton(value: Array[String]): CatBoostRegressor.this.type
- Definition Classes
- QuantizationParamsTrait
- final def setPerObjectFeaturePenaltiesList(value: Array[Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setPerObjectFeaturePenaltiesMap(value: LinkedHashMap[String, Double]): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- def setPredictionCol(value: String): CatBoostRegressor
- Definition Classes
- Predictor
- final def setRandomSeed(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setRandomStrength(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setRsm(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSamplingFrequency(value: ESamplingFrequency): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSamplingUnit(value: ESamplingUnit): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSaveSnapshot(value: Boolean): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setScoreFunction(value: EScoreFunction): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSnapshotFile(value: String): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSnapshotInterval(value: Duration): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSparkPartitionCount(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setSubsample(value: Float): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setThreadCount(value: Int): CatBoostRegressor.this.type
- Definition Classes
- ThreadCountParams
- final def setTrainDir(value: String): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setTrainingDriverListeningPort(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setUseBestModel(value: Boolean): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setWorkerInitializationTimeout(value: Duration): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setWorkerListeningPort(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final def setWorkerMaxFailures(value: Int): CatBoostRegressor.this.type
- Definition Classes
- TrainingParamsTrait
- final val snapshotFile: Param[String]
- Definition Classes
- TrainingParamsTrait
- final val snapshotInterval: DurationParam
- Definition Classes
- TrainingParamsTrait
- final val sparkPartitionCount: IntParam
- Definition Classes
- TrainingParamsTrait
- final val subsample: FloatParam
- Definition Classes
- TrainingParamsTrait
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- final val threadCount: IntParam
- Definition Classes
- ThreadCountParams
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- def train(dataset: Dataset[_]): CatBoostRegressionModel
- Attributes
- protected
- Definition Classes
- CatBoostPredictorTrait → Predictor
- final val trainDir: Param[String]
- Definition Classes
- TrainingParamsTrait
- final val trainingDriverListeningPort: IntParam
- Definition Classes
- TrainingParamsTrait
- def transformSchema(schema: StructType): StructType
- Definition Classes
- Predictor → PipelineStage
- def transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- val uid: String
- Definition Classes
- CatBoostRegressor → Identifiable
- final val useBestModel: BooleanParam
- Definition Classes
- TrainingParamsTrait
- def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- PredictorParams
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final val weightCol: Param[String]
- Definition Classes
- HasWeightCol
- final val workerInitializationTimeout: DurationParam
- Definition Classes
- TrainingParamsTrait
- final val workerListeningPort: IntParam
- Definition Classes
- TrainingParamsTrait
- final val workerMaxFailures: IntParam
- Definition Classes
- TrainingParamsTrait
- def write: MLWriter
- Definition Classes
- DefaultParamsWritable → MLWritable