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[SPARK-5714][Mllib] Refactor initial step of LDA to remove redundant operations #4501

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[SPARK-5714][Mllib] Refactor initial step of LDA to remove redundant operations #4501

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Refactor initial step of LDA to remove redundant operations.
viirya Feb 10, 2015
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viirya Feb 11, 2015
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37 changes: 13 additions & 24 deletions mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -450,34 +450,23 @@ private[clustering] object LDA {

// Create vertices.
// Initially, we use random soft assignments of tokens to topics (random gamma).
val edgesWithGamma: RDD[(Edge[TokenCount], TopicCounts)] =
edges.mapPartitionsWithIndex { case (partIndex, partEdges) =>
val random = new Random(partIndex + randomSeed)
partEdges.map { edge =>
// Create a random gamma_{wjk}
(edge, normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0))
def createVertices(): RDD[(VertexId, TopicCounts)] = {
val verticesTMP: RDD[(VertexId, TopicCounts)] =
edges.mapPartitionsWithIndex { case (partIndex, partEdges) =>
val random = new Random(partIndex + randomSeed)
partEdges.flatMap { edge =>
val gamma = normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0)
val sum = gamma * edge.attr
Seq((edge.srcId, sum), (edge.dstId, sum))
}
}
}
def createVertices(sendToWhere: Edge[TokenCount] => VertexId): RDD[(VertexId, TopicCounts)] = {
val verticesTMP: RDD[(VertexId, (TokenCount, TopicCounts))] =
edgesWithGamma.map { case (edge, gamma: TopicCounts) =>
(sendToWhere(edge), (edge.attr, gamma))
}
verticesTMP.aggregateByKey(BDV.zeros[Double](k))(
(sum, t) => {
brzAxpy(t._1, t._2, sum)
sum
},
(sum0, sum1) => {
sum0 += sum1
}
)
verticesTMP.reduceByKey(_ + _)
}
val docVertices = createVertices(_.srcId)
val termVertices = createVertices(_.dstId)

val docTermVertices = createVertices()

// Partition such that edges are grouped by document
val graph = Graph(docVertices ++ termVertices, edges)
val graph = Graph(docTermVertices, edges)
.partitionBy(PartitionStrategy.EdgePartition1D)

new EMOptimizer(graph, k, vocabSize, docConcentration, topicConcentration, checkpointInterval)
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