Github user kalmanchapman commented on a diff in the pull request:
https://github.com/apache/flink/pull/2735#discussion_r109329665
--- 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
+ .flatMap(x =>
+ x.zipWithIndex
+ .map(z => {
+ val window = (scala.math.random * 100 %
resultingParameters(WindowSize)).toInt
+ Context[String](
+ z._1, x.slice(z._2 - window, z._2) ++ x.slice(z._2 +1,
z._2 + window))
+ }))
+
+ val weights = new ContextEmbedder[String]
+ .setIterations(resultingParameters(Iterations))
+ .setTargetCount(resultingParameters(TargetCount))
+ .setVectorSize(resultingParameters(VectorSize))
+ .setLearningRate(resultingParameters(LearningRate))
+ .setBatchSize(resultingParameters(BatchSize))
+ .setSeed(resultingParameters(Seed))
+ .createInitialWeightsDS(instance.wordVectors, skipGrams)
+
+ instance.wordVectors = Some(weights)
+ }
+ }
+ }
+
+ /** [[TransformDataSetOperation]] for words to vectors
+ * form skipgrams from the input dataset and learn vectors against
+ * the vocabulary constructed during the fit operation
+ * returns a dataset of distinct words and their learned representations
+ *
+ * @tparam T subtype of Iterable[String]
+ * @return
+ */
+ implicit def words2Vecs[T <: Iterable[String]] = {
+ new TransformDataSetOperation[Word2Vec, T, (String, Vector[Double])] {
+ override def transformDataSet(instance: Word2Vec,
+ transformParameters: ParameterMap,
+ input: DataSet[T]): DataSet[(String,
Vector[Double])] = {
+ val resultingParameters = instance.parameters ++
transformParameters
+
+ instance.wordVectors match {
+ case Some(vectors) =>
+ val skipGrams = input
+ .flatMap(x =>
+ x.zipWithIndex
+ .map(z => {
+ val window = (scala.math.random * 100 %
resultingParameters(WindowSize)).toInt
+ Context[String](
+ z._1, x.slice(z._2 - window, z._2) ++ x.slice(z._2 +
1, z._2 + window))
+ }))
+
+ val learnedVectors = new ContextEmbedder[String]
+ .setIterations(resultingParameters(Iterations))
+ .setTargetCount(resultingParameters(TargetCount))
+ .setVectorSize(resultingParameters(VectorSize))
+ .setLearningRate(resultingParameters(LearningRate))
+ .setBatchSize(resultingParameters(BatchSize))
+ .setSeed(resultingParameters(Seed))
+ .optimize(skipGrams, instance.wordVectors)
+
+ learnedVectors
+ .flatMap(_.fetchVectors)
+ case None =>
--- End diff --
hey @kateri1 - I'm not 100% sure what you mean here.
We do check that the word vectors have been initialized via
`learnWordVectors`/`fitWord2Vec` - and throw an exception if the initialization
has not been run. This is similar to the logic used in
[MultipleLinearRegression](https://github.com/apache/flink/blob/master/flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/regression/MultipleLinearRegression.scala#L132)
fitter and other methods that require an initial fitting step.
Let me know if this answers the question you have.
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