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From: tillrohrmann <git@git.apache.org>
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Subject: [GitHub] flink pull request: [FLINK-1745] Add exact
 k-nearest-neighbours al...
Content-Type: text/plain
Message-Id: <20151123155309.586DBE0544@git1-us-west.apache.org>
Date: Mon, 23 Nov 2015 15:53:09 +0000 (UTC)

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

    https://github.com/apache/flink/pull/1220#discussion_r45619299
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn/KNN.scala ---
    @@ -0,0 +1,321 @@
    +/*
    + * 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.ml.nn.util.QuadTree
    +import scala.collection.mutable.ListBuffer
    +
    +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.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.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()''')
    +  *
    +  */
    +
    +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 = {
    +    parameters.add(UseQuadTreeParam, UseQuadTree)
    +    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
    +  }
    +
    +
    +  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))
    +
    +            // join input and training set
    +            val crossed = trainingSet.cross(inputSplit).mapPartition {
    --- End diff --
    
    if one of the `DataSets` is small, it might make sense to broadcast the smaller one. That way it wouldn't blow up the size of the intermediate result.


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