Added old JavaDecisionTreeSuite, to be updated later

mllib/src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java

@@ -0,0 +1,91 @@
+/*
+ * 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.mllib.tree;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.rdd.DatasetInfo;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.tree.configuration.DTClassifierParams;
+import org.apache.spark.mllib.tree.model.DecisionTreeClassifierModel;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.io.Serializable;
+import java.util.List;
+
+public class JavaDecisionTreeSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaDecisionTreeSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  int validatePrediction(List<LabeledPoint> validationData, DecisionTreeClassifierModel model) {
+    int numAccurate = 0;
+    for (LabeledPoint point: validationData) {
+      Double prediction = model.predict(point.features());
+      if (prediction == point.label()) {
+        numAccurate++;
+      }
+    }
+    return numAccurate;
+  }
+
+  @Test
+  public void runDTUsingConstructor() {
+    scala.Tuple2<java.util.List<LabeledPoint>, DatasetInfo> arr_datasetInfo =
+        DecisionTreeSuite.generateCategoricalDataPointsAsList();
+    JavaRDD<LabeledPoint> rdd = sc.parallelize(arr_datasetInfo._1());
+    DatasetInfo datasetInfo = arr_datasetInfo._2();
+
+    DTClassifierParams dtParams = DecisionTreeClassifier.defaultParams();
+    dtParams.setMaxBins(200);
+    dtParams.setImpurity("entropy");
+    DecisionTreeClassifier dtLearner = new DecisionTreeClassifier(dtParams);
+    DecisionTreeClassifierModel model = dtLearner.run(rdd.rdd(), datasetInfo);
+
+    int numAccurate = validatePrediction(arr_datasetInfo._1(), model);
+    Assert.assertTrue(numAccurate == rdd.count());
+  }
+/*
+  @Test
+  public void runDTUsingStaticMethods() {
+    scala.Tuple2<List<LabeledPoint>, DatasetInfo> arr_datasetInfo =
+        DecisionTreeSuite.generateCategoricalDataPointsAsList();
+    JavaRDD<LabeledPoint> rdd = sc.parallelize(arr_datasetInfo._1());
+    DatasetInfo datasetInfo = arr_datasetInfo._2();
+
+    DTClassifierParams dtParams = DecisionTreeClassifier.defaultParams();
+    DecisionTreeClassifierModel model =
+        DecisionTreeClassifier.train(rdd.rdd(), datasetInfo, dtParams);
+
+    int numAccurate = validatePrediction(arr_datasetInfo._1(), model);
+    Assert.assertTrue(numAccurate == rdd.count());
+  }
+*/
+}