Github user rotationsymmetry commented on a diff in the pull request:
https://github.com/apache/spark/pull/7769#discussion_r35947957
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -361,6 +361,44 @@ class DistributedLDAModel private (
}
}
+ /**
+ * Return the top documents for each topic
+ *
+ * This limits the number of documents per 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:
+ * (indices 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[Int],
Array[Double])] = {
+ val numTopics = k
+ val topicsInQueues: Array[BoundedPriorityQueue[(Double, Int)]] =
+ topicDistributions.mapPartitions { docVertices =>
+ // For this partition, collect the most common docs for each topic
in queues:
+ // queues(topic) = queue of (doc weight, doc index).
+ val queues =
+ Array.fill(numTopics)(new BoundedPriorityQueue[(Double,
Int)](maxDocumentsPerTopic))
+ for ((docId, docWeight) <- docVertices) {
--- End diff --
Revised as suggested. Thanks!
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