content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to You under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   *    http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  package org.apache.spark.ml.source.libsvm;
  
  import java.io.File;
  import java.io.IOException;
  import java.nio.charset.StandardCharsets;
  
  import com.google.common.io.Files;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.linalg.DenseVector;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.util.Utils;
  
  
  /**
   * Test LibSVMRelation in Java.
   */
  public class JavaLibSVMRelationSuite extends SharedSparkSession {
  
    private File tempDir;
    private String path;
  
    @Override
    public void setUp() throws IOException {
      super.setUp();
      tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
      File file = new File(tempDir, "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, file, StandardCharsets.UTF_8);
      path = tempDir.toURI().toString();
    }
  
    @Override
    public void tearDown() {
      super.tearDown();
      Utils.deleteRecursively(tempDir);
    }
  
    @Test
    public void verifyLibSVMDF() {
      Dataset<Row> dataset = spark.read().format("libsvm").option("vectorType", "dense")
        .load(path);
      Assert.assertEquals("label", dataset.columns()[0]);
      Assert.assertEquals("features", dataset.columns()[1]);
      Row r = dataset.first();
      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);
    }
  }
  