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SVM

  • Model API type

    The LinearSVC algorithm provides ML classification APIs.

    Model API Type

    Function API

    ML Classification API

    def fit(dataset: Dataset[_]): LinearSVCModel

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

    def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]) : LinearSVCModel

    def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]* ) : LinearSVCModel
  • Input: the model parameters of the fit API paramMap, paramMaps, firstParamPair, otherParamPairs, which are described as follows:

    Parameter

    Value Type

    Example

    Description

    paramMap

    ParamMap

    ParamMap(A.c -> b)

    Assigns the value of b to the parameter c of model A.

    paramMaps

    Array[ParamMap]

    Array[ParamMap](n)

    Generates n parameter lists for the ParamMap model.

    firstParamPair

    ParamPair

    ParamPair(A.c, b)

    Assigns the value of b to the parameter c of model A.

    otherParamPairs

    ParamPair

    ParamPair(A.e, f)

    Assigns the value of f to the parameter e of model A.

ML Classification API

  • Function description

    Output the LinearSVC classification model after you input sample data in dataset format and call the fit API.

  • Input and output
    1. Package name: package org.apache.spark.ml.classification
    2. Class name: LinearSVC
    3. Method name: fit
    4. Input: training sample data (Dataset[_]). The following are mandatory fields.

      Parameter

      Value Type

      Default Value

      Description

      labelCol

      Double

      label

      Predicted label

      featuresCol

      Vector

      features

      Feature label
    5. Parameters optimized based on native algorithms
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      def setRegParam(value: Double): this.type
def setMaxIter(value: Int): this.type
def setFitIntercept(value: Boolean): this.type
def setTol(value: Double): this.type
def setStandardization(value: Boolean): this.type
def setWeightCol(value: String): this.type
def setThreshold(value: Double): this.type
def setAggregationDepth(value: Int): this.type
    6. Newly added parameters

      Parameter

      Description

      Value Type

      inertiaCoefficient

      Weight of historical direction information in momentum calculation

      Double type. The value is a positive real number.
      An example is provided as follows:
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      import org.apache.spark.ml.param.{ParamMap, ParamPair}

val svm = new LinearSVC()

// Define the def fit(dataset: Dataset[_], paramMap: ParamMap) API parameter.
val paramMap = ParamMap(svm.regParam -> regParam)
.put(svm.maxIter, numIterations)

// Define the def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): API parameter.
val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
for (i <- 0 to  2) {
paramMaps(i) = ParamMap(svm.regParam -> regParam(i))
.put(svm.maxIter, numIterations)
}//Assign a value to paramMaps.

// Define the def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) API parameter.
val defaultParamPair = ParamPair(svm.regParam, regParam)
val regParamPair = ParamPair(svm.regParam, regParam)
val maxIterParamPair = ParamPair(svm.maxIter, numIterations)

// Call the fit APIs.
model = svm.fit(trainingData)  // Return GBTRegressionModel.
model = svm.fit(trainingData, paramMap)  // Return GBTRegressionModel.
models = svm.fit(trainingData, paramMaps)  // Return Seq[GBTRegressionModel].
model = svm.fit(trainingData, defaultParamPair, regParamPair, maxIterParamPair) // Return SVMRegressionModel.
    7. Output: LinearSVC classification model (LinearSVCModel). The following table lists the fields output in model prediction.

      Parameter

      Value Type

      Default Value

      Description

      Remarks

      prediction

      Double

      prediction

      Predicted label

      -

      label

      Double

      label

      label

      Classification only

      features

      Vector

      features

      features

      Classification only
  • Example

    fit(dataset: Dataset[_]): LinearSVCModel example:

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    test("train"){
val svc = new LinearSVC().setMaxIter(100).setRegParam(0.1)
svc.setIc(0.1)
val msvc = svc.fit(sdf)
val res = msvc.transform(sdf)
val accuracy = res.filter($"label"===$"prediction").count().toDouble/res.count
println("svmx Accuracy = "+accuracy)
}
  • Result
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    svmx Accuracy = 0.999406629786391