[SPARK-19382][ML] Test sparse vectors in LinearSVCSuite

## What changes were proposed in this pull request?

Add unit tests for testing SparseVector.

We can't add mixed DenseVector and SparseVector test case, as discussed in JIRA 19382.

 def merge(other: MultivariateOnlineSummarizer): this.type = {
if (this.totalWeightSum != 0.0 && other.totalWeightSum != 0.0) {
require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " +
s"Expecting $n but got $
{other.n}

.")

## How was this patch tested?

Unit tests

Author: wm624@hotmail.com <wm624@hotmail.com>
Author: Miao Wang <wangmiao1981@users.noreply.github.com>

Closes #16784 from wangmiao1981/bk.

Author: wangmiao1981

mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala

@@ -24,12 +24,13 @@ import breeze.linalg.{DenseVector => BDV}
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.classification.LinearSVCSuite._
 import org.apache.spark.ml.feature.{Instance, LabeledPoint}
-import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions.udf
 
 
 class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@@ -41,6 +42,9 @@ class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with Defau
   @transient var smallValidationDataset: Dataset[_] = _
   @transient var binaryDataset: Dataset[_] = _
 
+  @transient var smallSparseBinaryDataset: Dataset[_] = _
+  @transient var smallSparseValidationDataset: Dataset[_] = _
+
   override def beforeAll(): Unit = {
     super.beforeAll()
 
@@ -51,6 +55,13 @@ class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with Defau
     smallBinaryDataset = generateSVMInput(A, Array[Double](B, C), nPoints, 42).toDF()
     smallValidationDataset = generateSVMInput(A, Array[Double](B, C), nPoints, 17).toDF()
     binaryDataset = generateSVMInput(1.0, Array[Double](1.0, 2.0, 3.0, 4.0), 10000, 42).toDF()
+
+    // Dataset for testing SparseVector
+    val toSparse: Vector => SparseVector = _.asInstanceOf[DenseVector].toSparse
+    val sparse = udf(toSparse)
+    smallSparseBinaryDataset = smallBinaryDataset.withColumn("features", sparse('features))
+    smallSparseValidationDataset = smallValidationDataset.withColumn("features", sparse('features))
+
   }
 
   /**
@@ -68,13 +79,17 @@ class LinearSVCSuite extends SparkFunSuite with MLlibTestSparkContext with Defau
     val model = svm.fit(smallBinaryDataset)
     assert(model.transform(smallValidationDataset)
       .where("prediction=label").count() > nPoints * 0.8)
+    val sparseModel = svm.fit(smallSparseBinaryDataset)
+    checkModels(model, sparseModel)
   }
 
   test("Linear SVC binary classification with regularization") {
     val svm = new LinearSVC()
     val model = svm.setRegParam(0.1).fit(smallBinaryDataset)
     assert(model.transform(smallValidationDataset)
       .where("prediction=label").count() > nPoints * 0.8)
+    val sparseModel = svm.fit(smallSparseBinaryDataset)
+    checkModels(model, sparseModel)
   }
 
   test("params") {
@@ -235,7 +250,7 @@ object LinearSVCSuite {
     "aggregationDepth" -> 3
   )
 
-    // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise)
+  // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise)
   def generateSVMInput(
       intercept: Double,
       weights: Array[Double],
@@ -252,5 +267,10 @@ object LinearSVCSuite {
     y.zip(x).map(p => LabeledPoint(p._1, Vectors.dense(p._2)))
   }
 
+  def checkModels(model1: LinearSVCModel, model2: LinearSVCModel): Unit = {
+    assert(model1.intercept == model2.intercept)
+    assert(model1.coefficients.equals(model2.coefficients))
+  }
+
 }