OpLogisticRegression

Instance Constructors

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

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
def checkThresholdConsistency(): Unit

Attributes
protected
Definition Classes
LogisticRegressionParams
final def clear(param: Param[_]): OpLogisticRegression.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def copy(extra: ParamMap): OpLogisticRegression.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 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 family: Param[String]

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
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[LogisticRegressionModel]

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[LogisticRegressionModel]]

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

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

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 getFamily: String

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
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
def getLowerBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getLowerBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
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
OpPipelineStage2 → OpPipelineStageBase
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 getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
def getSparkMlStage(): Option[LogisticRegression]

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

Definition Classes
SparkWrapperParams
final def getStandardization: Boolean

Definition Classes
HasStandardization
def getThreshold: Double

Definition Classes
LogisticRegressionParams → HasThreshold
def getThresholds: Array[Double]

Definition Classes
LogisticRegressionParams → HasThresholds
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
def getUpperBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getUpperBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
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): 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
val lowerBoundsOnCoefficients: Param[Matrix]

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val lowerBoundsOnIntercepts: Param[Vector]

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
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
OpLogisticRegression → OpPipelineStageBase
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: LogisticRegression

the predictor to wrap
the predictor to wrap

Definition Classes
OpPredictorWrapper
final val probabilityCol: Param[String]

Definition Classes
HasProbabilityCol
final val rawPredictionCol: Param[String]

Definition Classes
HasRawPredictionCol
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[_]): OpLogisticRegression.this.type

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

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

Definition Classes
Params
def setAggregationDepth(value: Int): OpLogisticRegression.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[_]*): OpLogisticRegression.this.type

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

Attributes
protected
Definition Classes
Params
def setElasticNetParam(value: Double): OpLogisticRegression.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.
Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.
def setFamily(value: String): OpLogisticRegression.this.type

Sets the value of param family.
Sets the value of param family. Default is "auto".
def setFitIntercept(value: Boolean): OpLogisticRegression.this.type

Whether to fit an intercept term.
Whether to fit an intercept term. Default is true.
final def setInput(features: InputFeatures): OpLogisticRegression.this.type

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

Attributes
protected
Definition Classes
InputParams
def setLowerBoundsOnCoefficients(value: Matrix): OpLogisticRegression.this.type

Set the lower bounds on coefficients if fitting under bound constrained optimization.
def setLowerBoundsOnIntercepts(value: Vector): OpLogisticRegression.this.type

Set the lower bounds on intercepts if fitting under bound constrained optimization.
def setMaxIter(value: Int): OpLogisticRegression.this.type

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

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

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

Set the regularization parameter.
Set the regularization parameter. Default is 0.0.
def setSparkMlStage(stage: Option[LogisticRegression]): OpLogisticRegression.this.type

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

Definition Classes
SparkWrapperParams
def setStandardization(value: Boolean): OpLogisticRegression.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. Note that 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. Default is true.
def setThreshold(value: Double): OpLogisticRegression.this.type

Definition Classes
OpLogisticRegression → LogisticRegressionParams
def setThresholds(value: Array[Double]): OpLogisticRegression.this.type

Definition Classes
OpLogisticRegression → LogisticRegressionParams
def setTol(value: Double): OpLogisticRegression.this.type

Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.
def setUpperBoundsOnCoefficients(value: Matrix): OpLogisticRegression.this.type

Set the upper bounds on coefficients if fitting under bound constrained optimization.
def setUpperBoundsOnIntercepts(value: Vector): OpLogisticRegression.this.type

Set the upper bounds on intercepts if fitting under bound constrained optimization.
def setWeightCol(value: String): OpLogisticRegression.this.type

Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.
final val sparkInputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final val sparkMlStage: SparkStageParam[LogisticRegression]

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
final val threshold: DoubleParam

Definition Classes
HasThreshold
final val thresholds: DoubleArrayParam

Definition Classes
HasThresholds
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 → OpPipelineStage2
implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

Definition Classes
OpPredictorWrapper → OpPipelineStage2
val uid: String

stage uid
stage uid

Definition Classes
OpPredictorWrapper → Identifiable
val upperBoundsOnCoefficients: Param[Matrix]

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val upperBoundsOnIntercepts: Param[Vector]

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def usingBoundConstrainedOptimization: Boolean

Attributes
protected
Definition Classes
LogisticRegressionParams
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → 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 classification

class OpLogisticRegression extends OpPredictorWrapper[LogisticRegression, LogisticRegressionModel] with OpLogisticRegressionParams

Instance Constructors

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

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]

def checkThresholdConsistency(): Unit

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

def clone(): AnyRef

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

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

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

final val elasticNetParam: 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 family: Param[String]

final val featuresCol: Param[String]

def finalize(): Unit

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

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

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

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

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 getFamily: String

final def getFeaturesCol: String

final def getFitIntercept: Boolean

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

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getLabelCol: String

def getLowerBoundsOnCoefficients: Matrix

def getLowerBoundsOnIntercepts: Vector

final def getMaxIter: Int

final def getMetadata(): Metadata

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

def getOutput(): FeatureLike[Prediction]

final def getOutputFeatureName: String

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

final def getPredictionCol: String

final def getProbabilityCol: String

final def getRawPredictionCol: String

final def getRegParam: Double

def getSparkMlStage(): Option[LogisticRegression]

def getStageSavePath(): Option[String]

final def getStandardization: Boolean

def getThreshold: Double

def getThresholds: Array[Double]

final def getTol: Double

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

final def getTransientFeatures(): Array[TransientFeature]

def getUpperBoundsOnCoefficients: Matrix

def getUpperBoundsOnIntercepts: Vector

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): Unit

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