Github user EntilZha commented on a diff in the pull request:
https://github.com/apache/spark/pull/4047#discussion_r23502479
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
---
@@ -0,0 +1,472 @@
+/*
+ * 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 java.util.Random
+
+import breeze.linalg.{DenseVector => BDV, sum => brzSum, normalize, axpy
=> brzAxpy}
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.graphx._
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
+
+
+/**
+ * :: DeveloperApi ::
+ *
+ * Latent Dirichlet Allocation (LDA), a topic model designed for text
documents.
+ *
+ * Terminology:
+ * - "word" = "term": an element of the vocabulary
+ * - "token": instance of a term appearing in a document
+ * - "topic": multinomial distribution over words representing some
concept
+ *
+ * Currently, the underlying implementation uses Expectation-Maximization
(EM), implemented
+ * according to the Asuncion et al. (2009) paper referenced below.
+ *
+ * References:
+ * - Original LDA paper (journal version):
+ * Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
+ * - This class implements their "smoothed" LDA model.
+ * - Paper which clearly explains several algorithms, including EM:
+ * Asuncion, Welling, Smyth, and Teh.
+ * "On Smoothing and Inference for Topic Models." UAI, 2009.
+ *
+ * NOTE: This is currently marked DeveloperApi since it is under active
development and may undergo
+ * API changes.
+ */
+@DeveloperApi
+class LDA private (
+ private var k: Int,
+ private var maxIterations: Int,
+ private var topicSmoothing: Double,
+ private var termSmoothing: Double,
+ private var seed: Long) extends Logging {
+
+ import LDA._
+
+ def this() = this(k = 10, maxIterations = 20, topicSmoothing = -1,
termSmoothing = -1,
+ seed = Utils.random.nextLong())
+
+ /**
+ * Number of topics to infer. I.e., the number of soft cluster centers.
+ * (default = 10)
+ */
+ def getK: Int = k
+
+ def setK(k: Int): this.type = {
+ require(k > 0, s"LDA k (number of clusters) must be > 0, but was set
to $k")
+ this.k = k
+ this
+ }
+
+ /**
+ * Topic smoothing parameter (commonly named "alpha").
+ *
+ * This is the parameter to the Dirichlet prior placed on the
per-document topic distributions
+ * ("theta"). We use a symmetric Dirichlet prior.
+ *
+ * This value should be > 0.0, where larger values mean more smoothing
(more regularization).
+ * If set to -1, then topicSmoothing is set automatically.
+ * (default = -1 = automatic)
+ *
+ * Automatic setting of parameter:
+ * - For EM: default = (50 / k) + 1.
+ * - The 50/k is common in LDA libraries.
+ * - The +1 follows Asuncion et al. (2009), who recommend a +1
adjustment for EM.
+ */
+ def getTopicSmoothing: Double = topicSmoothing
+
+ def setTopicSmoothing(topicSmoothing: Double): this.type = {
+ require(topicSmoothing > 0.0 || topicSmoothing == -1.0,
+ s"LDA topicSmoothing must be > 0 (or -1 for auto), but was set to
$topicSmoothing")
+ if (topicSmoothing > 0.0 && topicSmoothing <= 1.0) {
+ logWarning(s"LDA.topicSmoothing was set to $topicSmoothing, but for
EM, we recommend > 1.0")
+ }
+ this.topicSmoothing = topicSmoothing
+ this
+ }
+
+ /**
+ * Term smoothing parameter (commonly named "eta").
+ *
+ * This is the parameter to the Dirichlet prior placed on the per-topic
word distributions
+ * (which are called "beta" in the original LDA paper by Blei et al.,
but are called "phi" in many
+ * later papers such as Asuncion et al., 2009.)
+ *
+ * This value should be > 0.0.
+ * If set to -1, then termSmoothing is set automatically.
+ * (default = -1 = automatic)
+ *
+ * Automatic setting of parameter:
+ * - For EM: default = 0.1 + 1.
+ * - The 0.1 gives a small amount of smoothing.
+ * - The +1 follows Asuncion et al. (2009), who recommend a +1
adjustment for EM.
+ */
+ def getTermSmoothing: Double = termSmoothing
+
+ def setTermSmoothing(termSmoothing: Double): this.type = {
+ require(termSmoothing > 0.0 || termSmoothing == -1.0,
+ s"LDA termSmoothing must be > 0 (or -1 for auto), but was set to
$termSmoothing")
+ if (termSmoothing > 0.0 && termSmoothing <= 1.0) {
+ logWarning(s"LDA.termSmoothing was set to $termSmoothing, but for
EM, we recommend > 1.0")
+ }
+ this.termSmoothing = termSmoothing
+ this
+ }
+
+ /**
+ * Maximum number of iterations for learning.
+ * (default = 20)
+ */
+ def getMaxIterations: Int = maxIterations
+
+ def setMaxIterations(maxIterations: Int): this.type = {
+ this.maxIterations = maxIterations
+ this
+ }
+
+ /** Random seed */
+ def getSeed: Long = seed
+
+ def setSeed(seed: Long): this.type = {
+ this.seed = seed
+ this
+ }
+
+ /**
+ * Learn an LDA model using the given dataset.
+ *
+ * @param documents RDD of documents, where each document is
represented as a vector of term
+ * counts plus an ID. Document IDs must be >= 0.
+ * @return Inferred LDA model
+ */
+ def run(documents: RDD[Document]): DistributedLDAModel = {
+ val topicSmoothing = if (this.topicSmoothing > 0) {
+ this.topicSmoothing
+ } else {
+ (50.0 / k) + 1.0
+ }
+ val termSmoothing = if (this.termSmoothing > 0) {
+ this.termSmoothing
+ } else {
+ 1.1
+ }
+ var state = LDA.initialState(documents, k, topicSmoothing,
termSmoothing, seed)
+ var iter = 0
+ while (iter < maxIterations) {
+ state = state.next()
+ iter += 1
+ }
+ new DistributedLDAModel(state)
+ }
+}
+
+
+object LDA {
+
+ /*
+ DEVELOPERS NOTE:
+
+ This implementation uses GraphX, where the graph is bipartite with 2
types of vertices:
+ - Document vertices
+ - indexed {0, 1, ..., numDocuments-1}
+ - Store vectors of length k (# topics).
+ - Term vertices
+ - indexed {-1, -2, ..., -vocabSize}
+ - Store vectors of length k (# topics).
+ - Edges correspond to terms appearing in documents.
+ - Edges are directed Document -> Term.
+ - Edges are partitioned by documents.
+
+ Info on EM implementation.
+ - We follow Section 2.2 from Asuncion et al., 2009. We use some of
their notation.
+ - In this implementation, there is one edge for every unique term
appearing in a document,
+ i.e., for every unique (document, term) pair.
+ - Notation:
+ - N_{wkj} = count of tokens of term w currently assigned to topic
k in document j
+ - N_{*} where * is missing a subscript w/k/j is the count summed
over missing subscript(s)
+ - gamma_{wjk} = P(z_i = k | x_i = w, d_i = j),
+ the probability of term x_i in document d_i having topic z_i.
+ - Data graph
+ - Document vertices store N_{kj}
+ - Term vertices store N_{wk}
+ - Edges store N_{wj}.
+ - Global data N_k
+ - Algorithm
+ - Initial state:
+ - Document and term vertices store random counts N_{wk}, N_{kj}.
+ - E-step: For each (document,term) pair i, compute P(z_i | x_i,
d_i).
+ - Aggregate N_k from term vertices.
+ - Compute gamma_{wjk} for each possible topic k, from each
triplet.
+ using inputs N_{wk}, N_{kj}, N_k.
+ - M-step: Compute sufficient statistics for hidden parameters phi
and theta
+ (counts N_{wk}, N_{kj}, N_k).
+ - Document update:
+ - N_{kj} <- sum_w N_{wj} gamma_{wjk}
+ - N_j <- sum_k N_{kj} (only needed to output predictions)
+ - Term update:
+ - N_{wk} <- sum_j N_{wj} gamma_{wjk}
+ - N_k <- sum_w N_{wk}
+ */
+
+ /**
+ * :: DeveloperApi ::
--- End diff --
Primarily, convenience, there are some data formats where creating from an
edge list is more natural. For example, one input format which I have worked
with is formatted like:
```
DocId WordId NumOccurrences
```
That input format is very easy to do with edges, but requires more work to
group by DocId, then perform an aggregate. In the case of receiving documents
as RDD[String], I think both methods are comparable in complexity if we don't
provide an interface for doing so in first release. Summary, I think they are
both useful different ways to input data.
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