Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/4047#discussion_r23724967 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala --- @@ -0,0 +1,265 @@ +/* + * 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.clustering + +import breeze.linalg.{DenseMatrix => BDM, normalize} + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix} +import org.apache.spark.rdd.RDD +import org.apache.spark.util.BoundedPriorityQueue + +/** + * :: DeveloperApi :: + * + * Latent Dirichlet Allocation (LDA) model. + * + * This abstraction permits for different underlying representations, + * including local and distributed data structures. + */ +@DeveloperApi +abstract class LDAModel private[clustering] { + + import LDA._ + + /** Number of topics */ + def k: Int + + /** Vocabulary size (number of terms or terms in the vocabulary) */ + def vocabSize: Int + + /** + * Inferred topics, where each topic is represented by a distribution over terms. + * This is a matrix of size vocabSize x k, where each column is a topic. + * No guarantees are given about the ordering of the topics. + */ + def topicsMatrix: Matrix + + /** + * Return the topics described by weighted terms. + * + * This limits the number of terms per topic. + * This is approximate; it may not return exactly the top-weighted terms for each topic. + * To get a more precise set of top terms, increase maxTermsPerTopic. + * + * @param maxTermsPerTopic Maximum number of terms to collect for each topic. + * @return Array over topics, where each element is a set of top terms represented + * as (term weight in topic, term index). + * Each topic's terms are sorted in order of decreasing weight. + */ + def describeTopics(maxTermsPerTopic: Int): Array[Array[(Double, String)]] --- End diff -- Why `String` instead of an `Int`? It is not clear from the doc how the string is constructed. I'm thinking about using `Array[Vector]` as the return type and use sparse vectors to represent the distribution with thresholding. One minor downside is that the term indices are not ordered by term weights.
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