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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1745) Add exact k-nearest-neighbours algorithm to machine learning library
Date Wed, 18 May 2016 14:17:13 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15289033#comment-15289033
] 

ASF GitHub Bot commented on FLINK-1745:
---------------------------------------

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

    https://github.com/apache/flink/pull/1220#discussion_r63710402
  
    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/nn/KNN.scala ---
    @@ -0,0 +1,354 @@
    +/*
    + * 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.nn
    +
    +import org.apache.flink.api.common.operators.Order
    +import org.apache.flink.api.common.typeinfo.TypeInformation
    +import org.apache.flink.api.scala.utils._
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.common._
    +import org.apache.flink.ml.math.{Vector => FlinkVector, DenseVector}
    +import org.apache.flink.ml.metrics.distances.{SquaredEuclideanDistanceMetric, DistanceMetric,
    +EuclideanDistanceMetric}
    +import org.apache.flink.ml.pipeline.{FitOperation, PredictDataSetOperation, Predictor}
    +import org.apache.flink.util.Collector
    +import org.apache.flink.api.common.operators.base.CrossOperatorBase.CrossHint
    +
    +import scala.collection.immutable.Vector
    +import scala.collection.mutable
    +import scala.collection.mutable.ArrayBuffer
    +import scala.reflect.ClassTag
    +
    +/** Implements a k-nearest neighbor join.
    +  *
    +  * Calculates the `k`-nearest neighbor points in the training set for each point in
the test set.
    +  *
    +  * @example
    +  * {{{
    +  *       val trainingDS: DataSet[Vector] = ...
    +  *       val testingDS: DataSet[Vector] = ...
    +  *
    +  *       val knn = KNN()
    +  *         .setK(10)
    +  *         .setBlocks(5)
    +  *         .setDistanceMetric(EuclideanDistanceMetric())
    +  *
    +  *       knn.fit(trainingDS)
    +  *
    +  *       val predictionDS: DataSet[(Vector, Array[Vector])] = knn.predict(testingDS)
    +  * }}}
    +  *
    +  * =Parameters=
    +  *
    +  * - [[org.apache.flink.ml.nn.KNN.K]]
    +  * Sets the K which is the number of selected points as neighbors. (Default value: '''5''')
    +  *
    +  * - [[org.apache.flink.ml.nn.KNN.DistanceMetric]]
    +  * Sets the distance metric we use to calculate the distance between two points. If
no metric is
    +  * specified, then [[org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric]]
is used.
    +  * (Default value: '''EuclideanDistanceMetric()''')
    +  *
    +  * - [[org.apache.flink.ml.nn.KNN.Blocks]]
    +  * Sets the number of blocks into which the input data will be split. This number should
be set
    +  * at least to the degree of parallelism. If no value is specified, then the parallelism
of the
    +  * input [[DataSet]] is used as the number of blocks. (Default value: '''None''')
    +  *
    +  * - [[org.apache.flink.ml.nn.KNN.UseQuadTreeParam]]
    +  * A boolean variable that whether or not to use a Quadtree to partition the training
set
    +  * to potentially simplify the KNN search.  If no value is specified, the code will
    +  * automatically decide whether or not to use a Quadtree.  Use of a Quadtree scales
well
    +  * with the number of training and testing points, though poorly with the dimension.
    +  * (Default value:  ```None```)
    +  *
    +  * - [[org.apache.flink.ml.nn.KNN.SizeHint]]
    +  * Specifies whether the training set or test set is small to optimize the cross
    +  * product operation needed for the KNN search.  If the training set is small
    +  * this should be `CrossHint.FIRST_IS_SMALL` and set to `CrossHint.SECOND_IS_SMALL`
    +  * if the test set is small.
    +  * (Default value:  ```None```)
    +  *
    +  */
    +
    +class KNN extends Predictor[KNN] {
    +
    +  import KNN._
    +
    +  var trainingSet: Option[DataSet[Block[FlinkVector]]] = None
    +
    +  /** Sets K
    +    * @param k the number of selected points as neighbors
    +    */
    +  def setK(k: Int): KNN = {
    +    require(k > 0, "K must be positive.")
    +    parameters.add(K, k)
    +    this
    +  }
    +
    +  /** Sets the distance metric
    +    * @param metric the distance metric to calculate distance between two points
    +    */
    +  def setDistanceMetric(metric: DistanceMetric): KNN = {
    +    parameters.add(DistanceMetric, metric)
    +    this
    +  }
    +
    +  /** Sets the number of data blocks/partitions
    +    * @param n the number of data blocks
    +    */
    +  def setBlocks(n: Int): KNN = {
    +    require(n > 0, "Number of blocks must be positive.")
    +    parameters.add(Blocks, n)
    +    this
    +  }
    +
    +  /**
    +   * Sets the Boolean variable that decides whether to use the QuadTree or not
    +   */
    +  def setUseQuadTree(useQuadTree: Boolean): KNN = {
    +    if (useQuadTree){
    +      require(parameters(DistanceMetric).isInstanceOf[SquaredEuclideanDistanceMetric]
||
    +        parameters(DistanceMetric).isInstanceOf[EuclideanDistanceMetric])
    +    }
    +    parameters.add(UseQuadTreeParam, useQuadTree)
    +    this
    +  }
    +
    +  /**
    +   * Parameter a user can specify if one of the training or test sets are small
    +   * @param sizeHint
    +   * @return
    +   */
    +  def setSizeHint(sizeHint: CrossHint): KNN = {
    +    parameters.add(SizeHint, sizeHint)
    +    this
    +  }
    +
    +}
    +
    +object KNN {
    +
    +  case object K extends Parameter[Int] {
    +    val defaultValue: Option[Int] = Some(5)
    +  }
    +
    +  case object DistanceMetric extends Parameter[DistanceMetric] {
    +    val defaultValue: Option[DistanceMetric] = Some(EuclideanDistanceMetric())
    +  }
    +
    +  case object Blocks extends Parameter[Int] {
    +    val defaultValue: Option[Int] = None
    +  }
    +
    +  case object UseQuadTreeParam extends Parameter[Boolean] {
    +    val defaultValue: Option[Boolean] = None
    +  }
    +
    +  case object SizeHint extends Parameter[CrossHint] {
    +    val defaultValue: Option[CrossHint] = None
    +  }
    +
    +  def apply(): KNN = {
    +    new KNN()
    +  }
    +
    +  /** [[FitOperation]] which trains a KNN based on the given training data set.
    +    * @tparam T Subtype of [[org.apache.flink.ml.math.Vector]]
    +    */
    +  implicit def fitKNN[T <: FlinkVector : TypeInformation] = new FitOperation[KNN,
T] {
    +    override def fit(
    +      instance: KNN,
    +      fitParameters: ParameterMap,
    +      input: DataSet[T]): Unit = {
    +      val resultParameters = instance.parameters ++ fitParameters
    +
    +      require(resultParameters.get(K).isDefined, "K is needed for calculation")
    +
    +      val blocks = resultParameters.get(Blocks).getOrElse(input.getParallelism)
    +      val partitioner = FlinkMLTools.ModuloKeyPartitioner
    +      val inputAsVector = input.asInstanceOf[DataSet[FlinkVector]]
    +
    +      instance.trainingSet = Some(FlinkMLTools.block(inputAsVector, blocks, Some(partitioner)))
    +    }
    +  }
    +
    +  /** [[PredictDataSetOperation]] which calculates k-nearest neighbors of the given testing
data
    +    * set.
    +    * @tparam T Subtype of [[Vector]]
    +    * @return The given testing data set with k-nearest neighbors
    +    */
    +  implicit def predictValues[T <: FlinkVector : ClassTag : TypeInformation] = {
    +    new PredictDataSetOperation[KNN, T, (FlinkVector, Array[FlinkVector])] {
    +      override def predictDataSet(
    +        instance: KNN,
    +        predictParameters: ParameterMap,
    +        input: DataSet[T]): DataSet[(FlinkVector,
    +        Array[FlinkVector])] = {
    +        val resultParameters = instance.parameters ++ predictParameters
    +
    +        instance.trainingSet match {
    +          case Some(trainingSet) =>
    +            val k = resultParameters.get(K).get
    +            val blocks = resultParameters.get(Blocks).getOrElse(input.getParallelism)
    +            val metric = resultParameters.get(DistanceMetric).get
    +            val partitioner = FlinkMLTools.ModuloKeyPartitioner
    +
    +            // attach unique id for each data
    +            val inputWithId: DataSet[(Long, T)] = input.zipWithUniqueId
    +
    +            // split data into multiple blocks
    +            val inputSplit = FlinkMLTools.block(inputWithId, blocks, Some(partitioner))
    +
    +            val sizeHint = resultParameters.get(SizeHint)
    +            val crossTuned = sizeHint match {
    +              case Some(hint) if hint == CrossHint.FIRST_IS_SMALL =>
    +                trainingSet.crossWithHuge(inputSplit)
    +              case Some(hint) if hint == CrossHint.SECOND_IS_SMALL =>
    +                trainingSet.crossWithTiny(inputSplit)
    +              case _ => trainingSet.cross(inputSplit)
    +            }
    +
    +            // join input and training set
    +            val crossed = crossTuned.mapPartition {
    +              (iter, out: Collector[(FlinkVector, FlinkVector, Long, Double)]) =>
{
    +                for ((training, testing) <- iter) {
    +                  val queue = mutable.PriorityQueue[(FlinkVector, FlinkVector, Long,
Double)]()(
    +                    Ordering.by(_._4))
    +
    +                  // use a quadtree if (4^dim)Ntest*log(Ntrain)
    +                  // < Ntest*Ntrain, and distance is Euclidean
    +                  val useQuadTree = resultParameters.get(UseQuadTreeParam).getOrElse(
    +                    training.values.head.size + math.log(math.log(training.values.length)
/
    +                      math.log(4.0)) < math.log(training.values.length) / math.log(4.0)
&&
    +                      (metric.isInstanceOf[EuclideanDistanceMetric] ||
    +                        metric.isInstanceOf[SquaredEuclideanDistanceMetric]))
    +
    +                  if (useQuadTree) {
    +                    knnQueryWithQuadTree(training.values, testing.values, k, metric,
queue, out)
    +                  } else {
    +                    knnQueryBasic(training.values, testing.values, k, metric, queue,
out)
    +                  }
    +                }
    +              }
    +            }
    +
    +            // group by input vector id and pick k nearest neighbor for each group
    +            val result = crossed.groupBy(2).sortGroup(3, Order.ASCENDING).reduceGroup
{
    +              (iter, out: Collector[(FlinkVector, Array[FlinkVector])]) => {
    +                if (iter.hasNext) {
    +                  val head = iter.next()
    +                  val key = head._2
    +                  val neighbors: ArrayBuffer[FlinkVector] = ArrayBuffer(head._1)
    +
    +                  for ((vector, _, _, _) <- iter.take(k - 1)) {
    +                    // we already took a first element
    +                    neighbors += vector
    +                  }
    +
    +                  out.collect(key, neighbors.toArray)
    +                }
    +              }
    +            }
    +
    +            result
    +          case None => throw new RuntimeException("The KNN model has not been trained."
+
    +            "Call first fit before calling the predict operation.")
    +
    +        }
    +      }
    +    }
    +  }
    +
    +  def knnQueryWithQuadTree[T <: FlinkVector](
    +    training: Vector[T],
    +    testing: Vector[(Long, T)],
    +    k: Int, metric: DistanceMetric,
    --- End diff --
    
    done, ran `cmd + alt + shift + L` in IntelliJ


> Add exact k-nearest-neighbours algorithm to machine learning library
> --------------------------------------------------------------------
>
>                 Key: FLINK-1745
>                 URL: https://issues.apache.org/jira/browse/FLINK-1745
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Daniel Blazevski
>              Labels: ML, Starter
>
> Even though the k-nearest-neighbours (kNN) [1,2] algorithm is quite trivial it is still
used as a mean to classify data and to do regression. This issue focuses on the implementation
of an exact kNN (H-BNLJ, H-BRJ) algorithm as proposed in [2].
> Could be a starter task.
> Resources:
> [1] [http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
> [2] [https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf]



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