flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From danielblazevski <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-1745] Add exact k-nearest-neighbours al...
Date Mon, 02 Nov 2015 16:34:52 GMT
Github user danielblazevski commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1220#discussion_r43648312
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn/KNN.scala ---
    @@ -0,0 +1,297 @@
    +/*
    + * 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.{Breeze,Vector, 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.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[Vector]]] = 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 <: Vector : 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[Vector]]
    +
    +      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 <: Vector : ClassTag : TypeInformation] = {
    +    new PredictDataSetOperation[KNN, T, (Vector, Array[Vector])] {
    +      override def predictDataSet(
    +          instance: KNN,
    +          predictParameters: ParameterMap,
    +          input: DataSet[T]): DataSet[(Vector, Array[Vector])] = {
    +        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 {
    +              (iter, out: Collector[(Vector, Vector, Long, Double)]) => {
    +                for ((training, testing) <- iter) {
    +                  val queue = mutable.PriorityQueue[(Vector, Vector, Long, Double)]()(
    +                    Ordering.by(_._4))
    +
    +                  var MinArr =  List.range(0,training.values.head.size).toArray
    +                  var MaxArr =  List.range(0,training.values.head.size).toArray
    +
    +                  var trainingFiltered = new ListBuffer[Vector]
    --- End diff --
    
    thanks @chiwanpark, fixed both the `var` and capitalization of `useQuadTree`


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

Mime
View raw message