Fix some reviews

mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala

@@ -20,6 +20,7 @@ package org.apache.spark.ml.source.libsvm
 import com.google.common.base.Objects
 
 import org.apache.spark.Logging
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
@@ -68,8 +69,10 @@ private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vec
  * This is used for creating DataFrame from LibSVM format file.
  * The LibSVM file path must be specified to DefaultSource.
  */
+@Since("1.6.0")
 class DefaultSource extends RelationProvider with DataSourceRegister {
 
+  @Since("1.6.0")
   override def shortName(): String = "libsvm"
 
   private def checkPath(parameters: Map[String, String]): String = {

mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java

@@ -23,13 +23,13 @@
 import com.google.common.base.Charsets;
 import com.google.common.io.Files;
 
-import org.apache.spark.mllib.linalg.DenseVector;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.DenseVector;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
@@ -45,15 +45,16 @@ public class JavaLibSVMRelationSuite {
   private transient SQLContext jsql;
   private transient DataFrame dataset;
 
+  private File tmpDir;
   private File path;
 
   @Before
   public void setUp() throws IOException {
     jsc = new JavaSparkContext("local", "JavaLibSVMRelationSuite");
     jsql = new SQLContext(jsc);
 
-    File tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
-    path = File.createTempFile("datasource", "libsvm-relation", tmpDir);
+    tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
+    path = new File(tmpDir.getPath(), "part-00000");
 
     String s = "1 1:1.0 3:2.0 5:3.0\n0\n0 2:4.0 4:5.0 6:6.0";
     Files.write(s, path, Charsets.US_ASCII);
@@ -63,7 +64,7 @@ public void setUp() throws IOException {
   public void tearDown() {
     jsc.stop();
     jsc = null;
-    path.delete();
+    Utils.deleteRecursively(tmpDir);
   }
 
   @Test
@@ -72,7 +73,7 @@ public void verifyLibSVMDF() {
     Assert.assertEquals("label", dataset.columns()[0]);
     Assert.assertEquals("features", dataset.columns()[1]);
     Row r = dataset.first();
-    Assert.assertEquals(Double.valueOf(1.0), Double.valueOf(r.getDouble(0)));
+    Assert.assertEquals(1.0, r.getDouble(0), 1e-15);
     DenseVector v = r.getAs(1);
     Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v);
   }

mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala

@@ -21,8 +21,8 @@ import java.io.File
 
 import com.google.common.base.Charsets
 import com.google.common.io.Files
+
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.source.libsvm._
 import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.util.Utils
@@ -67,19 +67,10 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("select long vector with specifying the number of features") {
-    val lines =
-      """
-        |1 1:1 10:2 20:3 30:4 40:5 50:6 60:7 70:8 80:9 90:10 100:1
-        |0 1:1 10:10 20:9 30:8 40:7 50:6 60:5 70:4 80:3 90:2 100:1
-      """.stripMargin
-    val tempDir = Utils.createTempDir()
-    val file = new File(tempDir.getPath, "part-00001")
-    Files.write(lines, file, Charsets.US_ASCII)
     val df = sqlContext.read.option("numFeatures", "100").format("libsvm")
-      .load(tempDir.toURI.toString)
+      .load(path)
     val row1 = df.first()
     val v = row1.getAs[SparseVector](1)
-    assert(v == Vectors.sparse(100, Seq((0, 1.0), (9, 2.0), (19, 3.0), (29, 4.0), (39, 5.0),
-      (49, 6.0), (59, 7.0), (69, 8.0), (79, 9.0), (89, 10.0), (99, 1.0))))
+    assert(v == Vectors.sparse(100, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
   }
 }