CatBoostRegressionModel

Regression model trained by CatBoost. Use CatBoostRegressor to train it

Serialization

Supports standard Spark MLLib serialization. Data can be saved to distributed filesystem like HDFS or local files. When saved to path two files are created: -<path>/metadata which contains Spark-specific metadata in JSON format -<path>/model which contains model in usual CatBoost format which can be read using other local CatBoost APIs (if stored in a distributed filesystem it has to be copied to the local filesystem first).

Examples:

Load model

val dataFrameForPrediction : DataFrame = ... init DataFrame ...
val path = "/home/user/catboost_spark_models/model0"
val model = CatBoostRegressionModel.load(path)
val predictions = model.transform(dataFrameForPrediction)
predictions.show()

Save model

val trainPool : Pool = ... init Pool ...
val regressor = new CatBoostRegressor
val model = regressor.fit(trainPool)
val path = "/home/user/catboost_spark_models/model0"
model.write.save(path)

Linear Supertypes

CatBoostModelTrait[CatBoostRegressionModel], MLWritable, RegressionModel[Vector, CatBoostRegressionModel], PredictionModel[Vector, CatBoostRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[CatBoostRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

Instance Constructors

new CatBoostRegressionModel(nativeModel: TFullModel)
new CatBoostRegressionModel(uid: String, nativeModel: TFullModel = null, nativeDimension: Int)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): CatBoostRegressionModel.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): CatBoostRegressionModel

Definition Classes
CatBoostRegressionModel → Model → Transformer → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def featuresDataType: DataType

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
def getAdditionalColumnsForApply: Seq[StructField]

Attributes
protected
Definition Classes
CatBoostRegressionModel → CatBoostModelTrait
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
def getResultIteratorForApply(rawObjectsDataProvider: SWIGTYPE_p_NCB__TRawObjectsDataProviderPtr, dstRows: ArrayBuffer[Array[Any]], threadCountForTask: Int): Iterator[Row]

Attributes
protected
Definition Classes
CatBoostRegressionModel → CatBoostModelTrait
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hasParent: Boolean

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]

Definition Classes
HasLabelCol
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
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
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
var nativeDimension: Int

Attributes
protected
Definition Classes
CatBoostRegressionModel → CatBoostModelTrait
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numFeatures: Int

Definition Classes
PredictionModel
Annotations
@Since( "1.6.0" )
lazy val params: Array[Param[_]]

Definition Classes
Params
var parent: Estimator[CatBoostRegressionModel]

Definition Classes
Model
def predict(features: Vector): Double

Prefer batch computations operating on datasets as a whole for efficiency
Prefer batch computations operating on datasets as a whole for efficiency

Definition Classes
CatBoostRegressionModel → PredictionModel
final def predictRawImpl(features: Vector): Array[Double]

Prefer batch computations operating on datasets as a whole for efficiency
Prefer batch computations operating on datasets as a whole for efficiency

Definition Classes
CatBoostModelTrait
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): CatBoostRegressionModel.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): CatBoostRegressionModel.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): CatBoostRegressionModel.this.type

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): CatBoostRegressionModel.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): CatBoostRegressionModel.this.type

Attributes
protected
Definition Classes
Params
def setFeaturesCol(value: String): CatBoostRegressionModel

Definition Classes
PredictionModel
def setParent(parent: Estimator[CatBoostRegressionModel]): CatBoostRegressionModel

Definition Classes
Model
def setPredictionCol(value: String): CatBoostRegressionModel

Definition Classes
PredictionModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame

Definition Classes
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

Definition Classes
CatBoostModelTrait → 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
CatBoostRegressionModel → Identifiable
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( ... )
def write: MLWriter

Definition Classes
CatBoostModelTrait → MLWritable

Related Docs: object CatBoostRegressionModel | package spark

class CatBoostRegressionModel extends RegressionModel[Vector, CatBoostRegressionModel] with CatBoostModelTrait[CatBoostRegressionModel]

Serialization

Instance Constructors

new CatBoostRegressionModel(nativeModel: TFullModel)

new CatBoostRegressionModel(uid: String, nativeModel: TFullModel = null, nativeDimension: Int)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): CatBoostRegressionModel.this.type

def clone(): AnyRef

def copy(extra: ParamMap): CatBoostRegressionModel

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def featuresDataType: DataType

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

def getAdditionalColumnsForApply: Seq[StructField]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

final def getFeaturesCol: String

final def getLabelCol: String

final def getOrDefault[T](param: Param[T]): T

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

def getResultIteratorForApply(rawObjectsDataProvider: SWIGTYPE_p_NCB__TRawObjectsDataProviderPtr, dstRows: ArrayBuffer[Array[Any]], threadCountForTask: Int): Iterator[Row]

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

var nativeDimension: Int

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numFeatures: Int

lazy val params: Array[Param[_]]

var parent: Estimator[CatBoostRegressionModel]

def predict(features: Vector): Double

final def predictRawImpl(features: Vector): Array[Double]

final val predictionCol: Param[String]

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): CatBoostRegressionModel.this.type

final def set(param: String, value: Any): CatBoostRegressionModel.this.type

final def set[T](param: Param[T], value: T): CatBoostRegressionModel.this.type

final def setDefault(paramPairs: ParamPair[_]*): CatBoostRegressionModel.this.type

final def setDefault[T](param: Param[T], value: T): CatBoostRegressionModel.this.type

def setFeaturesCol(value: String): CatBoostRegressionModel

def setParent(parent: Estimator[CatBoostRegressionModel]): CatBoostRegressionModel

def setPredictionCol(value: String): CatBoostRegressionModel

final def synchronized[T0](arg0: ⇒ T0): T0