Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4047#discussion_r23732311
--- 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 --
I see your point. We can have something similar to sklearn's
`CountVectorizer`
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
It transforms `RDD[Array[String]]` to `RDD[Vector]` and stores the word <->
index bi-map in the model it produces. The model can provide transformations
from words to indices and indices to words. Then LDA can use integer ids only.
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