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From: sachingoel0101 <git@git.apache.org>
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Subject: [GitHub] flink pull request: [FLINK-1719] [ml] Add Multinomial Naive
 Bayes ...
Content-Type: text/plain
Message-Id: <20150925152803.2DCF5DFE5F@git1-us-west.apache.org>
Date: Fri, 25 Sep 2015 15:28:03 +0000 (UTC)

Github user sachingoel0101 commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1156#discussion_r40442050
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/classification/MultinomialNaiveBayes.scala ---
    @@ -0,0 +1,900 @@
    +/*
    + * 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.flink.ml.classification
    +
    +import java.{lang, util}
    +
    +import org.apache.flink.api.common.functions._
    +import org.apache.flink.api.scala._
    +import org.apache.flink.configuration.Configuration
    +import org.apache.flink.core.fs.FileSystem.WriteMode
    +import org.apache.flink.ml.common.{ParameterMap, Parameter}
    +import org.apache.flink.ml.pipeline.{PredictDataSetOperation, FitOperation, Predictor}
    +import org.apache.flink.util.Collector
    +
    +import scala.collection.JavaConverters._
    +import scala.collection.mutable
    +import scala.collection.mutable.ListBuffer
    +import scala.collection.mutable.Map
    +
    +/**
    + * While building the model different approaches need to be compared.
    + * For that purpose the fitParameters are used. Every possibility that might enhance
    + * the implementation can be chosen separately by using the following list of parameters:
    + *
    + * Possibility 1: way of calculating document count
    + *  P1 = 0 -> use .count() to get count of all documents
    + *  P1 = 1 -> use a reducer and a mapper to create a broadcast data set containing the count of
    + *    all documents
    + *
    + * Possibility 2: all words in class (order of operators)
    + *    If p2 = 1 improves the speed, many other calculations must switch their operators, too.
    + *  P2 = 0 -> first the reducer, than the mapper
    + *  P2 = 1 -> first the mapper, than the reducer
    + *
    + * Possibility 3: way of calculating pwc
    + *  P2 = 0 -> join singleWordsInClass and allWordsInClass to wordsInClass data set
    + *  P3 = 1 -> work on singleWordsInClass data set and broadcast allWordsInClass data set
    + *
    + * Schneider/Rennie 1: ignore/reduce word frequency information
    + *  SR1 = 0 -> word frequency information is not ignored
    + *  SR1 = 1 -> word frequency information is ignored (Schneiders approach)
    + *  SR1 = 2 -> word frequency information is reduced (Rennies approach)
    + *
    + * Schneider1: ignore P(c_j) in cMAP formula
    + *  S1 = 0 -> normal cMAP formula
    + *  S2 = 1 -> cMAP without P(c_j)
    + *
    + * Rennie1: transform document frequency
    + *  R1 = 0 -> normal formula
    + *  R1 = 1 -> apply inverse document frequecy
    + * Note: if R1 = 1 and SR1 = 2, both approaches get applied.
    + *
    + */
    +class MultinomialNaiveBayes extends Predictor[MultinomialNaiveBayes] {
    +
    +  import MultinomialNaiveBayes._
    +
    +  //The model, that stores all needed information that are related to one specific word
    +  var wordRelatedModelData: Option[DataSet[(String, String, Double)]] =
    +    None // (class name -> word -> log P(w|c))
    +
    +  //The model, that stores all needed information that are related to one specifc class+
    +  var classRelatedModelData: Option[DataSet[(String, Double, Double)]] =
    +    None // (class name -> p(c) -> log p(w|c) not in class)
    +
    +  //A data set that stores additional needed information for some of the improvements
    +  var improvementData: Option[DataSet[(String, Double)]] =
    +    None // (word -> log number of documents in all classes / word frequency in all classes
    +
    +  // ============================== Parameter configuration ========================================
    +
    +  def setP1(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(P1, value)
    +    this
    +  }
    +
    +  def setP2(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(P2, value)
    +    this
    +  }
    +
    +  def setP3(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(P3, value)
    +    this
    +  }
    +
    +  def setSR1(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(SR1, value)
    +    this
    +  }
    +
    +  def setS1(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(S1, value)
    +    this
    +  }
    +
    +  def setR1(value: Int): MultinomialNaiveBayes = {
    +    parameters.add(R1, value)
    +    this
    +  }
    +
    +  // =============================================== Methods =======================================
    +
    +  /**
    +   * Save already existing model data created by the NaiveBayes algorithm. Requires the designated
    +   * locations. The saved data is a representation of the [[wordRelatedModelData]] and
    +   * [[classRelatedModelData]].
    +   * @param wordRelated, the save location for the wordRelated data
    +   * @param classRelated, the save location for the classRelated data
    +   */
    +  def saveModelDataSet(wordRelated: String, classRelated: String) : Unit = {
    +    wordRelatedModelData.get.writeAsCsv(wordRelated, "\n", "|", WriteMode.OVERWRITE)
    +    classRelatedModelData.get.writeAsCsv(classRelated, "\n", "|", WriteMode.OVERWRITE)
    +  }
    +
    +  /**
    +   * Save the improvment data set. Requires the designated save location. The saved data is a
    +   * representation of the [[improvementData]] data set.
    +   * @param path, the save location for the improvment data
    +   */
    +  def saveImprovementDataSet(path: String) : Unit = {
    +    improvementData.get.writeAsCsv(path, "\n", "|", WriteMode.OVERWRITE)
    +  }
    +
    +  /**
    +   * Sets the [[wordRelatedModelData]] and the [[classRelatedModelData]] to the given data sets.
    +   * @param wordRelated, the data set representing the wordRelated model
    +   * @param classRelated, the data set representing the classRelated model
    +   */
    +  def setModelDataSet(wordRelated : DataSet[(String, String, Double)],
    +                      classRelated: DataSet[(String, Double, Double)]) : Unit = {
    +    this.wordRelatedModelData = Some(wordRelated)
    +    this.classRelatedModelData = Some(classRelated)
    +  }
    +
    +  def setImprovementDataSet(impSet : DataSet[(String, Double)]) : Unit = {
    +    this.improvementData = Some(impSet)
    +  }
    +
    +}
    +
    +object MultinomialNaiveBayes {
    +
    +  // ========================================== Parameters =========================================
    +  case object P1 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  case object P2 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  case object P3 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  case object SR1 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  case object S1 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  case object R1 extends Parameter[Int] {
    +    override val defaultValue: Option[Int] = Some(0)
    +  }
    +
    +  // ======================================== Factory Methods ======================================
    +  def apply(): MultinomialNaiveBayes = {
    +    new MultinomialNaiveBayes()
    +  }
    +
    +  // ====================================== Operations =============================================
    +  /**
    +   * Trains the models to fit the training data. The resulting
    +   * [[MultinomialNaiveBayes.wordRelatedModelData]] and
    +   * [[MultinomialNaiveBayes.classRelatedModelData]] are stored in the [[MultinomialNaiveBayes]]
    +   * instance.
    +   */
    +
    +  implicit val fitNNB = new FitOperation[MultinomialNaiveBayes, (String, String)] {
    +    /**
    +     * The [[FitOperation]] used to create the model. Requires an instance of
    +     * [[MultinomialNaiveBayes]], a [[ParameterMap]] and the input data set. This data set
    +     * maps (string -> string) containing (label -> text, words separated by ",")
    +     * @param instance of [[MultinomialNaiveBayes]]
    +     * @param fitParameters, additional parameters
    +     * @param input, the to processed data set
    +     */
    +    override def fit(instance: MultinomialNaiveBayes,
    +                     fitParameters: ParameterMap,
    +                     input: DataSet[(String, String)]): Unit = {
    +
    +      val resultingParameters = instance.parameters ++ fitParameters
    +
    +      //Count the amount of documents for each class.
    +      // 1. Map: replace the document text by a 1
    +      // 2. Group-Reduce: sum the 1s by class
    +      val documentsPerClass: DataSet[(String, Int)] = input.map { input => (input._1, 1)}
    +        .groupBy(0).sum(1) // (class name -> count of documents)
    +
    +      //Count the amount of occurrences of each word for each class.
    +      // 1. FlatMap: split the document into its words and add a 1 to each tuple
    +      // 2. Group-Reduce: sum the 1s by class, word
    +      var singleWordsInClass: DataSet[(String, String, Int)] = input
    +        .flatMap(new SingleWordSplitter())
    +        .groupBy(0, 1).sum(2) // (class name -> word -> count of that word)
    +
    +      //POSSIBILITY 2: all words in class (order of operators)
    +      //SCHNEIDER/RENNIE 1: ignore/reduce word frequency information
    +        //the allWordsInClass data set does only contain distinct
    +        //words for schneiders approach: ndw(cj), nothing changes for rennies approach
    +
    +      val p2 = resultingParameters(P2)
    +
    --- End diff --
    
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