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Added Java-friendly run method to LDA.
Added Java test suite for LDA. Changed LDAModel.describeTopics to return Java-friendly type
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118 changes: 118 additions & 0 deletions
118
mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.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. | ||
| */ | ||
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| package org.apache.spark.mllib.clustering; | ||
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| import java.io.Serializable; | ||
| import java.util.ArrayList; | ||
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| import scala.Tuple2; | ||
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| import org.junit.After; | ||
| import static org.junit.Assert.assertEquals; | ||
| import static org.junit.Assert.assertArrayEquals; | ||
| import org.junit.Before; | ||
| import org.junit.Test; | ||
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| import org.apache.spark.api.java.JavaRDD; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.mllib.linalg.Matrix; | ||
| import org.apache.spark.mllib.linalg.Vector; | ||
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| public class JavaLDASuite implements Serializable { | ||
| private transient JavaSparkContext sc; | ||
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| @Before | ||
| public void setUp() { | ||
| sc = new JavaSparkContext("local", "JavaLDA"); | ||
| tinyCorpus = new ArrayList<Tuple2<Long, Vector>>(); | ||
| for (int i = 0; i < LDASuite$.MODULE$.tinyCorpus().length; i++) { | ||
| tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite$.MODULE$.tinyCorpus()[i]._1(), | ||
| LDASuite$.MODULE$.tinyCorpus()[i]._2())); | ||
| } | ||
| corpus = sc.parallelize(tinyCorpus, 2); | ||
| } | ||
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| @After | ||
| public void tearDown() { | ||
| sc.stop(); | ||
| sc = null; | ||
| } | ||
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| @Test | ||
| public void localLDAModel() { | ||
| LocalLDAModel model = new LocalLDAModel(LDASuite$.MODULE$.tinyTopics()); | ||
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| // Check: basic parameters | ||
| assertEquals(model.k(), tinyK); | ||
| assertEquals(model.vocabSize(), tinyVocabSize); | ||
| assertEquals(model.topicsMatrix(), tinyTopics); | ||
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| // Check: describeTopics() with all terms | ||
| Tuple2<int[], double[]>[] fullTopicSummary = model.describeTopics(); | ||
| assertEquals(fullTopicSummary.length, tinyK); | ||
| for (int i = 0; i < fullTopicSummary.length; i++) { | ||
| assertArrayEquals(fullTopicSummary[i]._1(), tinyTopicDescription[i]._1()); | ||
| assertArrayEquals(fullTopicSummary[i]._2(), tinyTopicDescription[i]._2(), 1e-5); | ||
| } | ||
| } | ||
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| @Test | ||
| public void distributedLDAModel() { | ||
| int k = 3; | ||
| double topicSmoothing = 1.2; | ||
| double termSmoothing = 1.2; | ||
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| // Train a model | ||
| LDA lda = new LDA(); | ||
| lda.setK(k) | ||
| .setDocConcentration(topicSmoothing) | ||
| .setTopicConcentration(termSmoothing) | ||
| .setMaxIterations(5) | ||
| .setSeed(12345); | ||
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| DistributedLDAModel model = lda.run(corpus); | ||
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| // Check: basic parameters | ||
| LocalLDAModel localModel = model.toLocal(); | ||
| assertEquals(model.k(), k); | ||
| assertEquals(localModel.k(), k); | ||
| assertEquals(model.vocabSize(), tinyVocabSize); | ||
| assertEquals(localModel.vocabSize(), tinyVocabSize); | ||
| assertEquals(model.topicsMatrix(), localModel.topicsMatrix()); | ||
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| // Check: topic summaries | ||
| Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics(); | ||
| assertEquals(roundedTopicSummary.length, k); | ||
| Tuple2<int[], double[]>[] roundedLocalTopicSummary = localModel.describeTopics(); | ||
| assertEquals(roundedLocalTopicSummary.length, k); | ||
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| // Check: log probabilities | ||
| assert(model.logLikelihood() < 0.0); | ||
| assert(model.logPrior() < 0.0); | ||
| } | ||
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| private static int tinyK = LDASuite$.MODULE$.tinyK(); | ||
| private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize(); | ||
| private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics(); | ||
| private static Tuple2<int[], double[]>[] tinyTopicDescription = | ||
| LDASuite$.MODULE$.tinyTopicDescription(); | ||
| private ArrayList<Tuple2<Long, Vector>> tinyCorpus; | ||
| JavaRDD<Tuple2<Long, Vector>> corpus; | ||
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| } |
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