Packages

class LinearSVC extends Classifier[Vector, LinearSVC, LinearSVCModel] with LinearSVCParams with DefaultParamsWritable

Linear SVM Classifier

This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

Since 3.1.0, it supports stacking instances into blocks and using GEMV for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.

Annotations
@Since( "2.2.0" )
Source
LinearSVC.scala
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Inherited
  1. LinearSVC
  2. DefaultParamsWritable
  3. MLWritable
  4. LinearSVCParams
  5. HasMaxBlockSizeInMB
  6. HasThreshold
  7. HasAggregationDepth
  8. HasWeightCol
  9. HasStandardization
  10. HasTol
  11. HasFitIntercept
  12. HasMaxIter
  13. HasRegParam
  14. Classifier
  15. ClassifierParams
  16. HasRawPredictionCol
  17. Predictor
  18. PredictorParams
  19. HasPredictionCol
  20. HasFeaturesCol
  21. HasLabelCol
  22. Estimator
  23. PipelineStage
  24. Logging
  25. Params
  26. Serializable
  27. Serializable
  28. Identifiable
  29. AnyRef
  30. Any
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  2. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  3. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  4. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  5. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  6. 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
  7. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  8. 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
  9. 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
  10. 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
  11. 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

Members

  1. 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
  2. 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
    LinearSVCPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "2.2.0" )
  3. 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
  4. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. 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
  7. def fit(dataset: Dataset[_]): LinearSVCModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  8. def fit(dataset: Dataset[_], paramMaps: Seq[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" )
  9. 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" )
  10. 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()
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. 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
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. 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.

  20. 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( ... )
  21. 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
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. def transformSchema(schema: StructType): StructType

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

    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
    PredictorPipelineStage
  24. 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
    LinearSVCIdentifiable
    Annotations
    @Since( "2.2.0" )
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setFeaturesCol(value: String): LinearSVC

    Definition Classes
    Predictor
  2. 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" )
  3. def setLabelCol(value: String): LinearSVC

    Definition Classes
    Predictor
  4. 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" )
  5. def setPredictionCol(value: String): LinearSVC

    Definition Classes
    Predictor
  6. def setRawPredictionCol(value: String): LinearSVC

    Definition Classes
    Classifier
  7. def setRegParam(value: Double): LinearSVC.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations
    @Since( "2.2.0" )
  8. 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" )
  9. def setThreshold(value: Double): LinearSVC.this.type

    Set threshold in binary classification.

    Set threshold in binary classification.

    Annotations
    @Since( "2.2.0" )
  10. 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" )
  11. 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" )

Parameter getters

  1. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  2. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  3. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  4. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  5. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  6. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  7. final def getRegParam: Double

    Definition Classes
    HasRegParam
  8. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  9. def getThreshold: Double

    Definition Classes
    HasThreshold
  10. final def getTol: Double

    Definition Classes
    HasTol
  11. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val aggregationDepth: IntParam

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

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

    Definition Classes
    HasAggregationDepth
  2. final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Definition Classes
    HasMaxBlockSizeInMB

(expert-only) Parameter setters

  1. 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" )
  2. def setMaxBlockSizeInMB(value: Double): LinearSVC.this.type

    Sets the value of param maxBlockSizeInMB.

    Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

    Annotations
    @Since( "3.1.0" )

(expert-only) Parameter getters

  1. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  2. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB