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From EntilZha <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-1405] [mllib] Latent Dirichlet Allocati...
Date Sun, 25 Jan 2015 01:23:25 GMT
Github user EntilZha commented on a diff in the pull request:

    https://github.com/apache/spark/pull/4047#discussion_r23501018
  
    --- 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 ::
    +   *
    +   * Document with an ID.
    +   *
    +   * @param counts  Vector of term (word) counts in the document.
    +   *                This is the "bag of words" representation.
    +   * @param id  Unique ID associated with this document.
    +   *            Documents should be indexed {0, 1, ..., numDocuments-1}.
    +   *
    +   * TODO: Can we remove the id and still be able to zip predicted topics with the Documents?
    +   *
    +   * NOTE: This is currently marked DeveloperApi since it is under active development
and may
    +   *       undergo API changes.
    +   */
    +  @DeveloperApi
    +  case class Document(counts: Vector, id: Long)
    +
    +  /**
    +   * Vector over topics (length k) of token counts.
    +   * The meaning of these counts can vary, and it may or may not be normalized to be
a distribution.
    +   */
    +  private[clustering] type TopicCounts = BDV[Double]
    --- End diff --
    
    Is there a reason to have this as a Double rather than an Int?
    + Makes normalizing easier.
    - Uncertain what signals what these counts mean (aka, whether its in a normalized state
or not)
    - Affects the Gibbs implementation since its part of the LDA API, which currently uses
Array[Int].
    
    Computationally, normalizing should cost about as much as returning a new array from an
array (or vector) of integer counts. Thoughts?


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