Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def $[T](param: Param[T]): T

   An alias for getOrDefault().

   An alias for getOrDefault().

   Attributes

   protected

   Definition Classes

   Params

4.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

5.  final val aggregationDepth: IntParam

   Param for suggested depth for treeAggregate (>= 2).

   Param for suggested depth for treeAggregate (>= 2).

   Definition Classes

   HasAggregationDepth

6.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7.  final def clear(param: Param[_]): LinearSVC.this.type

   Clears the user-supplied value for the input param.

   Clears the user-supplied value for the input param.

   Definition Classes

   Params

8.  def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

9.  def copy(extra: ParamMap): LinearSVC

   Creates a copy of this instance with the same UID and some extra params.

   Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

   Definition Classes

   LinearSVC → Predictor → Estimator → PipelineStage → Params

   Annotations

   @Since( "2.2.0" )

10.  def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes

    protected

    Definition Classes

    Params

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

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes

    protected

    Definition Classes

    Params

12.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

13.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

14.  def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes

    Params

15.  def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes

    Params

16.  def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes

    protected

    Definition Classes

    Classifier

    Note

    Throws SparkException if any label is a non-integer or is negative

17.  def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes

    protected

    Definition Classes

    Predictor

18.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

19.  final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes

    Params

20.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

21.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

22.  def fit(dataset: Dataset[_]): LinearSVCModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

23.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[LinearSVCModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" )

24.  def fit(dataset: Dataset[_], paramMap: ParamMap): LinearSVCModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" )

25.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): LinearSVCModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes

    Estimator

    Annotations

    @Since( "2.0.0" ) @varargs()

26.  final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes

    HasFitIntercept

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

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes

    Params

28.  final def getAggregationDepth: Int
    Definition Classes

    HasAggregationDepth

29.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

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

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes

    Params

31.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

32.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

33.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

34.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

35.  def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes

    protected

    Definition Classes

    Classifier

    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

36.  final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes

    Params

37.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

38.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

39.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

40.  final def getRegParam: Double
    Definition Classes

    HasRegParam

41.  final def getStandardization: Boolean
    Definition Classes

    HasStandardization

42.  def getThreshold: Double
    Definition Classes

    HasThreshold

43.  final def getTol: Double
    Definition Classes

    HasTol

44.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

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

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes

    Params

46.  def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes

    Params

47.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

48.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

    Attributes

    protected

    Definition Classes

    Logging

49.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes

    Params

51.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

53.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

54.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

55.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

57.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

59.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

61.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

62.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

64.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

66.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

67.  final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes

    HasMaxIter

68.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

69.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

70.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

71.  lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes

    Params

    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

72.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

73.  final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes

    HasRawPredictionCol

74.  final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes

    HasRegParam

75.  def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes

    MLWritable

    Annotations

    @Since( "1.6.0" ) @throws( ... )

76.  final def set(paramPair: ParamPair[_]): LinearSVC.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

77.  final def set(param: String, value: Any): LinearSVC.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

79.  def setAggregationDepth(value: Int): LinearSVC.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.

    Annotations

    @Since( "2.2.0" )

80.  final def setDefault(paramPairs: ParamPair[_]*): LinearSVC.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes

    protected

    Definition Classes

    Params

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

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes

    protected

    Definition Classes

    Params

82.  def setFeaturesCol(value: String): LinearSVC
    Definition Classes

    Predictor

83.  def setFitIntercept(value: Boolean): LinearSVC.this.type

    Whether to fit an intercept term.

    Whether to fit an intercept term. Default is true.

    Annotations

    @Since( "2.2.0" )

84.  def setLabelCol(value: String): LinearSVC
    Definition Classes

    Predictor

85.  def setMaxIter(value: Int): LinearSVC.this.type

    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations

    @Since( "2.2.0" )

86.  def setPredictionCol(value: String): LinearSVC
    Definition Classes

    Predictor

87.  def setRawPredictionCol(value: String): LinearSVC
    Definition Classes

    Classifier

88.  def setRegParam(value: Double): LinearSVC.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations

    @Since( "2.2.0" )

89.  def setStandardization(value: Boolean): LinearSVC.this.type

    Whether to standardize the training features before fitting the model.

    Whether to standardize the training features before fitting the model. Default is true.

    Annotations

    @Since( "2.2.0" )

90.  def setThreshold(value: Double): LinearSVC.this.type

    Set threshold in binary classification.

    Set threshold in binary classification.

    Annotations

    @Since( "2.2.0" )

91.  def setTol(value: Double): LinearSVC.this.type

    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller values will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

    Annotations

    @Since( "2.2.0" )

92.  def setWeightCol(value: String): LinearSVC.this.type

    Set the value of param weightCol.

    Set 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.

    Annotations

    @Since( "2.2.0" )

93.  final val standardization: BooleanParam

    Param for whether to standardize the training features before fitting the model.

    Param for whether to standardize the training features before fitting the model.

    Definition Classes

    HasStandardization

94.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

95.  final val threshold: DoubleParam

    Param for threshold in binary classification prediction.

    Param for threshold in binary classification prediction. For LinearSVC, this threshold is applied to the rawPrediction, rather than a probability. This threshold can be any real number, where Inf will make all predictions 0.0 and -Inf will make all predictions 1.0. Default: 0.0

    Definition Classes

    LinearSVCParams → HasThreshold

96.  def toString(): String

    Definition Classes

    Identifiable → AnyRef → Any

97.  final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes

    HasTol

98.  def train(dataset: Dataset[_]): LinearSVCModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes

    protected

    Definition Classes

    LinearSVC → Predictor

99.  def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes

    Predictor → PipelineStage

100.  def transformSchema(schema: StructType, logging: Boolean): StructType

     :: DeveloperApi ::

     :: DeveloperApi ::

     Derives the output schema from the input schema and parameters, optionally with logging.

     This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

     Attributes

     protected

     Definition Classes

     PipelineStage

     Annotations

     @DeveloperApi()

101.  val uid: String

     An immutable unique ID for the object and its derivatives.

     An immutable unique ID for the object and its derivatives.

     Definition Classes

     LinearSVC → Identifiable

     Annotations

     @Since( "2.2.0" )

102.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

     Validates and transforms the input schema with the provided param map.

     Validates and transforms the input schema with the provided param map.

     schema

     input schema

     fitting

     whether this is in fitting

     featuresDataType

     SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

     returns

     output schema

     Attributes

     protected

     Definition Classes

     ClassifierParams → PredictorParams

103.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

104.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

105.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @native() @throws( ... )

106.  final val weightCol: Param[String]

     Param for weight column name.

     Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

     Definition Classes

     HasWeightCol

107.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DefaultParamsWritable → MLWritable