OpLinearRegression

Instance Constructors

new OpLinearRegression(uid: String = UID[OpLinearRegression])

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

Definition Classes
OpPipelineStage2 → OpPipelineStage → InputParams
final type OutputFeatures = FeatureLike[Prediction]

Definition Classes
OpPipelineStage → OpPipelineStageBase

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 val aggregationDepth: IntParam

Definition Classes
HasAggregationDepth
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def checkInputLength(features: Array[_]): Boolean

Definition Classes
OpPipelineStage2 → InputParams
def checkSerializable: Try[Unit]

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
OpPipelineStageBase → 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 val elasticNetParam: DoubleParam

Definition Classes
HasElasticNetParam
final val epsilon: DoubleParam

Definition Classes
LinearRegressionParams
Annotations
@Since( "2.3.0" )
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 finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): OpPredictorWrapperModel[LinearRegressionModel]

Function that fits the binary model
Function that fits the binary model

Definition Classes
OpPredictorWrapper → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[LinearRegressionModel]]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[LinearRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[LinearRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final val fitIntercept: BooleanParam

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

Definition Classes
Params
final def getAggregationDepth: Int

Definition Classes
HasAggregationDepth
final def getClass(): Class[_]

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

Definition Classes
Params
final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam
def getEpsilon: Double

Definition Classes
LinearRegressionParams
Annotations
@Since( "2.3.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
def getInputColParamNames(): Array[String]

Definition Classes
SparkWrapperParams
final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

Definition Classes
InputParams
final def getInputFeatures(): Array[OPFeature]

Definition Classes
InputParams
final def getInputSchema(): StructType

Definition Classes
OpPipelineStageParams
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getLoss: String

Definition Classes
HasLoss
final def getMaxIter: Int

Definition Classes
HasMaxIter
final def getMetadata(): Metadata

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

Definition Classes
Params
def getOutput(): FeatureLike[Prediction]

Definition Classes
HasOut
def getOutputColParamNames(): Array[String]

Definition Classes
SparkWrapperParams
final def getOutputFeatureName: String

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

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
final def getSolver: String

Definition Classes
HasSolver
def getSparkMlStage(): Option[LinearRegression]

Definition Classes
SparkWrapperParams
def getStageSavePath(): Option[String]

Definition Classes
SparkWrapperParams
final def getStandardization: Boolean

Definition Classes
HasStandardization
final def getTol: Double

Definition Classes
HasTol
final def getTransientFeature(i: Int): Option[TransientFeature]

Definition Classes
InputParams
final def getTransientFeatures(): Array[TransientFeature]

Definition Classes
InputParams
final def getWeightCol: String

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

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

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
final def in1: TransientFeature

Attributes
protected
Definition Classes
HasIn1
final def in2: TransientFeature

Attributes
protected
Definition Classes
HasIn2
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

Attributes
protected
Definition Classes
Logging
final def inputAsArray(in: InputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage2 → InputParams
val inputParam1Name: String

Definition Classes
OpPredictorWrapper
val inputParam2Name: String

Definition Classes
OpPredictorWrapper
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
final val loss: Param[String]

Definition Classes
LinearRegressionParams → HasLoss
Annotations
@Since( "2.3.0" )
final val maxIter: IntParam

Definition Classes
HasMaxIter
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def onGetMetadata(): Unit

Attributes
protected
Definition Classes
OpPipelineStageParams
def onSetInput(): Unit

Attributes
protected
Definition Classes
OpLinearRegression → InputParams
val operationName: String

Definition Classes
OpPredictorWrapper → OpPipelineStageBase
final def outputAsArray(out: OutputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage → OpPipelineStageBase
def outputFeatureUid: String

Attributes
protected[com.salesforce.op]
Definition Classes
OpPipelineStage2 → OpPipelineStage
def outputIsResponse: Boolean

Definition Classes
OpPipelineStage
val outputParamName: String

Definition Classes
OpPredictorWrapper
lazy val params: Array[Param[_]]

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
val predictor: LinearRegression

the predictor to wrap
the predictor to wrap

Definition Classes
OpPredictorWrapper
final val regParam: DoubleParam

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

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

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

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

Definition Classes
Params
def setAggregationDepth(value: Int): OpLinearRegression.this.type

Suggested depth for treeAggregate (greater than or equal to 2).
Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.
final def setDefault(paramPairs: ParamPair[_]*): OpLinearRegression.this.type

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

Attributes
protected
Definition Classes
Params
def setElasticNetParam(value: Double): OpLinearRegression.this.type

Set the ElasticNet mixing parameter.
Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
def setFitIntercept(value: Boolean): OpLinearRegression.this.type

Set if we should fit the intercept.
Set if we should fit the intercept. Default is true.
final def setInput(features: InputFeatures): OpLinearRegression.this.type

Definition Classes
OpPipelineStageBase
final def setInputFeatures[S <: OPFeature](features: Array[S]): OpLinearRegression.this.type

Attributes
protected
Definition Classes
InputParams
def setMaxIter(value: Int): OpLinearRegression.this.type

Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.
final def setMetadata(m: Metadata): OpLinearRegression.this.type

Definition Classes
OpPipelineStageParams
def setOutputFeatureName(name: String): OpLinearRegression.this.type

Definition Classes
OpPipelineStage
def setRegParam(value: Double): OpLinearRegression.this.type

Set the regularization parameter.
Set the regularization parameter. Default is 0.0.
def setSolver(value: String): OpLinearRegression.this.type

Set the solver algorithm used for optimization.
Set the solver algorithm used for optimization. In case of linear regression, this can be "l-bfgs", "normal" and "auto".
- "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method.
- "normal" denotes using Normal Equation as an analytical solution to the linear regression problem. This solver is limited to LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER.
- "auto" (default) means that the solver algorithm is selected automatically. The Normal Equations solver will be used when possible, but this will automatically fall back to iterative optimization methods when needed.
def setSparkMlStage(stage: Option[LinearRegression]): OpLinearRegression.this.type

Attributes
protected
Definition Classes
SparkWrapperParams
def setStageSavePath(path: String): OpLinearRegression.this.type

Definition Classes
SparkWrapperParams
def setStandardization(value: Boolean): OpLinearRegression.this.type

Whether to standardize the training features before fitting the model.
Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Default is true.

Note
With/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well.
def setTol(value: Double): OpLinearRegression.this.type

Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
def setWeightCol(value: String): OpLinearRegression.this.type

Whether to over-/under-sample training instances according to the given weights in weightCol.
Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.
final val solver: Param[String]

Definition Classes
LinearRegressionParams → HasSolver
Annotations
@Since( "1.6.0" )
final val sparkInputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final val sparkMlStage: SparkStageParam[LinearRegression]

Definition Classes
SparkWrapperParams
final val sparkOutputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final def stageName: String

Definition Classes
OpPipelineStageBase
final val standardization: BooleanParam

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam

Definition Classes
HasTol
final def transformSchema(schema: StructType): StructType

Definition Classes
OpPipelineStageBase
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

Definition Classes
OpPredictorWrapper
implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

Definition Classes
OpPredictorWrapper
implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

Definition Classes
OpPredictorWrapper → HasOut
implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

Definition Classes
OpPredictorWrapper → HasOut
val uid: String

stage uid
stage uid

Definition Classes
OpPredictorWrapper → Identifiable
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
LinearRegressionParams → 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( ... )
final val weightCol: Param[String]

Definition Classes
HasWeightCol
final def write: MLWriter

Definition Classes
OpPipelineStageBase → MLWritable

Related Doc: package regression

class OpLinearRegression extends OpPredictorWrapper[LinearRegression, LinearRegressionModel] with OpLinearRegressionParams

Instance Constructors

new OpLinearRegression(uid: String = UID[OpLinearRegression])

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

final type OutputFeatures = FeatureLike[Prediction]

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

final val aggregationDepth: IntParam

final def asInstanceOf[T0]: T0

final def checkInputLength(features: Array[_]): Boolean

def checkSerializable: Try[Unit]

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

def clone(): AnyRef

final def copy(extra: ParamMap): OpLinearRegression.this.type

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

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

final val elasticNetParam: DoubleParam

final val epsilon: DoubleParam

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 finalize(): Unit

def fit(dataset: Dataset[_]): OpPredictorWrapperModel[LinearRegressionModel]

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[LinearRegressionModel]]

def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[LinearRegressionModel]

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[LinearRegressionModel]

final val fitIntercept: BooleanParam

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

final def getAggregationDepth: Int

final def getClass(): Class[_]

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

final def getElasticNetParam: Double

def getEpsilon: Double

final def getFeaturesCol: String

final def getFitIntercept: Boolean

def getInputColParamNames(): Array[String]

final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getLabelCol: String

final def getLoss: String

final def getMaxIter: Int

final def getMetadata(): Metadata

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

def getOutput(): FeatureLike[Prediction]

def getOutputColParamNames(): Array[String]

final def getOutputFeatureName: String

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

final def getPredictionCol: String

final def getRegParam: Double

final def getSolver: String

def getSparkMlStage(): Option[LinearRegression]

def getStageSavePath(): Option[String]

final def getStandardization: Boolean

final def getTol: Double

final def getTransientFeature(i: Int): Option[TransientFeature]

final def getTransientFeatures(): Array[TransientFeature]

final def getWeightCol: String

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

final def in1: TransientFeature

final def in2: TransientFeature

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def inputAsArray(in: InputFeatures): Array[OPFeature]

val inputParam1Name: String

val inputParam2Name: String

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

final def isInstanceOf[T0]: Boolean