ADD Java test suites of the KMeans API for spark.ml Pipeline

mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java

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+/*
+ * 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 org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+
+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, 1000, 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);
+    assertArrayEquals(new String[]{"features", "prediction"}, transformed.columns());
+  }
+}