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From kateri1 <...@git.apache.org>
Subject [GitHub] flink pull request #2735: [FLINK-2094] [ml] implements Word2Vec for FlinkML
Date Tue, 31 Jan 2017 08:12:14 GMT
Github user kateri1 commented on a diff in the pull request:

    https://github.com/apache/flink/pull/2735#discussion_r98613559
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/nlp/Word2Vec.scala
---
    @@ -0,0 +1,243 @@
    +/*
    + * 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.nlp
    +
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.common.{Parameter, ParameterMap}
    +import org.apache.flink.ml.optimization.{Context, ContextEmbedder, HSMWeightMatrix}
    +import org.apache.flink.ml.pipeline.{FitOperation, TransformDataSetOperation, Transformer}
    +
    +/**
    +  * Implements Word2Vec as a transformer on a DataSet[Iterable[String]]
    +  *
    +  * Calculates valuable vectorizations of individual words given
    +  * the context in which they appear
    +  *
    +  * @example
    +  * {{{
    +  *   //constructed of 'sentences' - where each string in the iterable is a word
    +  *   val stringsDS = DataSet[Iterable[String]] = ...
    +  *   val stringsDS2 = DataSet[Iterable[String]] = ...
    +  *
    +  *   val w2V = Word2Vec()
    +  *     .setIterations(5)
    +  *     .setTargetCount(10)
    +  *     .setSeed(500)
    +  *
    +  *   //internalizes an initial weightSet
    +  *   w2V.fit(stringsDS)
    +  *
    +  *   //note that the same DS can be used to fit and optimize
    +  *   //the number of learned vectors is limted to the vocab built in fit
    +  *   val wordVectors : DataSet[(String, Vector[Double])] = w2V.optimize(stringsDS2)
    +  * }}}
    +  *
    +  * =Parameters=
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.WindowSize]]
    +  * sets the size of window for skipGram formation: how far on either side of
    +  * a given word will we sample the context? (Default value: '''10''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.Iterations]]
    +  * sets the number of global iterations the training set is passed through - essentially
looping on
    +  * whole set, leveraging flink's iteration operator (Default value: '''10''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.TargetCount]]
    +  * sets the minimum number of occurences of a given target value before that value is
    +  * excluded from vocabulary (e.g. if this parameter is set to '5', and a target
    +  * appears in the training set less than 5 times, it is not included in vocabulary)
    +  * (Default value: '''5''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.VectorSize]]
    +  * sets the length of each learned vector (Default value: '''100''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.LearningRate]]
    +  * sets the rate of descent during backpropagation - this value decays linearly with
    +  * individual training sets, determined by BatchSize (Default value: '''0.015''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.BatchSize]]
    +  * sets the batch size of training sets - the input DataSet will be batched into
    +  * groups of this size for learning (Default value: '''1000''')
    +  *
    +  * - [[org.apache.flink.ml.nlp.Word2Vec.Seed]]
    +  * sets the seed for generating random vectors at initial weighting DataSet creation
    +  * (Default value: '''Some(scala.util.Random.nextLong)''')
    +  */
    +class Word2Vec extends Transformer[Word2Vec] {
    +  import Word2Vec._
    +
    +  private [nlp] var wordVectors:
    +    Option[DataSet[HSMWeightMatrix[String]]] = None
    +
    +  def setIterations(iterations: Int): this.type = {
    +    parameters.add(Iterations, iterations)
    +    this
    +  }
    +
    +  def setTargetCount(targetCount: Int): this.type = {
    +    parameters.add(TargetCount, targetCount)
    +    this
    +  }
    +
    +  def setVectorSize(vectorSize: Int): this.type = {
    +    parameters.add(VectorSize, vectorSize)
    +    this
    +  }
    +
    +  def setLearningRate(learningRate: Double): this.type = {
    +    parameters.add(LearningRate, learningRate)
    +    this
    +  }
    +
    +  def setWindowSize(windowSize: Int): this.type = {
    +    parameters.add(WindowSize, windowSize)
    +    this
    +  }
    +
    +  def setBatchSize(batchSize: Int): this.type = {
    +    parameters.add(BatchSize, batchSize)
    +    this
    +  }
    +
    +  def setSeed(seed: Long): this.type = {
    +    parameters.add(Seed, seed)
    +    this
    +  }
    +
    +}
    +
    +object Word2Vec {
    +  case object Iterations extends Parameter[Int] {
    +    val defaultValue = Some(10)
    +  }
    +
    +  case object TargetCount extends Parameter[Int] {
    +    val defaultValue = Some(5)
    +  }
    +
    +  case object VectorSize extends Parameter[Int] {
    +    val defaultValue = Some(100)
    +  }
    +
    +  case object LearningRate extends Parameter[Double] {
    +    val defaultValue = Some(0.015)
    +  }
    +
    +  case object WindowSize extends Parameter[Int] {
    +    val defaultValue = Some(10)
    +  }
    +
    +  case object BatchSize extends Parameter[Int] {
    +    val defaultValue = Some(1000)
    +  }
    +
    +  case object Seed extends Parameter[Long] {
    +    val defaultValue = Some(scala.util.Random.nextLong)
    +  }
    +
    +  def apply(): Word2Vec = {
    +    new Word2Vec()
    +  }
    +
    +  /** [[FitOperation]] which builds initial vocabulary for Word2Vec context embedding
    +    *
    +    * @tparam T Subtype of Iterable[String]
    +    * @return
    +    */
    +  implicit def learnWordVectors[T <: Iterable[String]] = {
    +    new FitOperation[Word2Vec, T] {
    +      override def fit(
    +        instance: Word2Vec,
    +        fitParameters: ParameterMap,
    +        input: DataSet[T])
    +      : Unit = {
    +        val resultingParameters = instance.parameters ++ fitParameters
    +        
    +        val skipGrams = input
    --- End diff --
    
    There is no check for input, what would be in case incomingDataset will be empty, at least
trainig is not required. May be it would be better to throw exception notifing about that
or may be write a waring.
    	Such a trivial encoding could be recource consuming, but not efficient. 


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