spark-gpu-master/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
/*
 * 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.clustering;

import java.io.Serializable;
import java.util.Arrays;
import java.util.List;

import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;

public class JavaKMeansSuite implements Serializable {

  private transient int k = 5;
  private transient JavaSparkContext sc;
  private transient DataFrame dataset;
  private transient SQLContext sql;

  @Before
  public void setUp() {
    sc = new JavaSparkContext("local", "JavaKMeansSuite");
    sql = new SQLContext(sc);

    dataset = KMeansSuite.generateKMeansData(sql, 50, 3, k);
  }

  @After
  public void tearDown() {
    sc.stop();
    sc = null;
  }

  @Test
  public void fitAndTransform() {
    KMeans kmeans = new KMeans().setK(k).setSeed(1);
    KMeansModel model = kmeans.fit(dataset);

    Vector[] centers = model.clusterCenters();
    assertEquals(k, centers.length);

    DataFrame transformed = model.transform(dataset);
    List<String> columns = Arrays.asList(transformed.columns());
    List<String> expectedColumns = Arrays.asList("features", "prediction");
    for (String column: expectedColumns) {
      assertTrue(columns.contains(column));
    }
  }
}
