Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5830#discussion_r30092226
--- Diff:
mllib/src/test/java/org/apache/spark/ml/reduction/JavaOneVsRestSuite.java ---
@@ -0,0 +1,86 @@
+/*
+ * 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.reduction;
+
+import java.io.Serializable;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import static scala.collection.JavaConversions.seqAsJavaList;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.classification.LogisticRegression;
+import static
org.apache.spark.mllib.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaOneVsRestSuite implements Serializable {
+
+ private transient JavaSparkContext jsc;
+ private transient SQLContext jsql;
+ private transient DataFrame dataset;
+ private transient JavaRDD<LabeledPoint> datasetRDD;
+
+ @Before
+ public void setUp() {
+ jsc = new JavaSparkContext("local", "JavaLOneVsRestSuite");
+ jsql = new SQLContext(jsc);
+ int nPoints = 3;
+
+ /**
+ * The following weights and xMean/xVariance are computed from
iris dataset with lambda = 0.2.
+ * As a result, we are actually drawing samples from probability
distribution of built model.
+ */
+ double[] weights = {
+ -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
+ -0.16624, -0.84355, -0.048509, -0.301789, 4.170682 };
+
+ double[] xMean = {5.843, 3.057, 3.758, 1.199};
+ double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
+ List<LabeledPoint> points =
seqAsJavaList(generateMultinomialLogisticInput(
+ weights, xMean, xVariance, true, nPoints, 42));
+ datasetRDD = jsc.parallelize(points, 2);
+ dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);
+ dataset.registerTempTable("dataset");
+ }
+
+ @After
+ public void tearDown() {
+ jsc.stop();
+ jsc = null;
+ }
+
+ @Test
+ public void oneVsRestDefaultParams() {
+ OneVsRest ova = new OneVsRest();
+ ova.setClassifier(new LogisticRegression());
+ assert(ova.getLabelCol() == "label");
+ assert(ova.getPredictionCol() == "prediction");
+ OneVsRestModel ovaModel = ova.fit(dataset);
+ ovaModel.transform(dataset).registerTempTable("prediction");
--- End diff --
Why use a temp table instead of just using the DataFrame API?
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