diff options
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala | 37 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala | 22 |
2 files changed, 59 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index ff7035d224..0cdac84eeb 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -516,6 +516,43 @@ class DistributedLDAModel private[clustering] ( } } + /** + * Return the top documents for each topic + * + * This is approximate; it may not return exactly the top-weighted documents for each topic. + * To get a more precise set of top documents, increase maxDocumentsPerTopic. + * + * @param maxDocumentsPerTopic Maximum number of documents to collect for each topic. + * @return Array over topics. Each element represent as a pair of matching arrays: + * (IDs for the documents, weights of the topic in these documents). + * For each topic, documents are sorted in order of decreasing topic weights. + */ + def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = { + val numTopics = k + val topicsInQueues: Array[BoundedPriorityQueue[(Double, Long)]] = + topicDistributions.mapPartitions { docVertices => + // For this partition, collect the most common docs for each topic in queues: + // queues(topic) = queue of (doc topic, doc ID). + val queues = + Array.fill(numTopics)(new BoundedPriorityQueue[(Double, Long)](maxDocumentsPerTopic)) + for ((docId, docTopics) <- docVertices) { + var topic = 0 + while (topic < numTopics) { + queues(topic) += (docTopics(topic) -> docId) + topic += 1 + } + } + Iterator(queues) + }.treeReduce { (q1, q2) => + q1.zip(q2).foreach { case (a, b) => a ++= b } + q1 + } + topicsInQueues.map { q => + val (docTopics, docs) = q.toArray.sortBy(-_._1).unzip + (docs.toArray, docTopics.toArray) + } + } + // TODO // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ??? diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 79d2a1cafd..f2b94707fd 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -122,6 +122,28 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext { // Check: log probabilities assert(model.logLikelihood < 0.0) assert(model.logPrior < 0.0) + + // Check: topDocumentsPerTopic + // Compare it with top documents per topic derived from topicDistributions + val topDocsByTopicDistributions = { n: Int => + Range(0, k).map { topic => + val (doc, docWeights) = topicDistributions.sortBy(-_._2(topic)).take(n).unzip + (doc.toArray, docWeights.map(_(topic)).toArray) + }.toArray + } + + // Top 3 documents per topic + model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach {case (t1, t2) => + assert(t1._1 === t2._1) + assert(t1._2 === t2._2) + } + + // All documents per topic + val q = tinyCorpus.length + model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach {case (t1, t2) => + assert(t1._1 === t2._1) + assert(t1._2 === t2._2) + } } test("vertex indexing") { |