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From thvasilo <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-1731] [ml] Implementation of Feature K-...
Date Mon, 29 Jun 2015 13:31:05 GMT
Github user thvasilo commented on a diff in the pull request:

    https://github.com/apache/flink/pull/700#discussion_r33463192
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.scala
---
    @@ -0,0 +1,247 @@
    +/*
    + * 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.clustering
    +
    +import org.apache.flink.api.common.functions.RichMapFunction
    +import org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields
    +import org.apache.flink.api.scala.{DataSet, _}
    +import org.apache.flink.configuration.Configuration
    +import org.apache.flink.ml.common.{LabeledVector, _}
    +import org.apache.flink.ml.math.Breeze._
    +import org.apache.flink.ml.math.Vector
    +import org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric
    +import org.apache.flink.ml.pipeline._
    +
    +import scala.collection.JavaConverters._
    +
    +
    +/**
    + * Implements the KMeans algorithm which calculates cluster centroids based on set of
training data
    + * points and a set of k initial centroids.
    + *
    + * [[KMeans]] is a [[Predictor]] which needs to be trained on a set of data points and
can then be
    + * used to assign new points to the learned cluster centroids.
    + *
    + * The KMeans algorithm works as described on Wikipedia
    + * (http://en.wikipedia.org/wiki/K-means_clustering):
    + *
    + * Given an initial set of k means m1(1),…,mk(1) (see below), the algorithm proceeds
by alternating
    + * between two steps:
    + *
    + * ===Assignment step:===
    + *
    + * Assign each observation to the cluster whose mean yields the least within-cluster
sum  of
    + * squares (WCSS). Since the sum of squares is the squared Euclidean distance, this is
intuitively
    + * the "nearest" mean. (Mathematically, this means partitioning the observations according
to the
    + * Voronoi diagram generated by the means).
    + *
    + * `S_i^(t) = { x_p : || x_p - m_i^(t) ||^2 ≤ || x_p - m_j^(t) ||^2 \forall j, 1 ≤
j ≤ k}`,
    + * where each `x_p`  is assigned to exactly one `S^{(t)}`, even if it could be assigned
to two or
    + * more of them.
    + *
    + * ===Update step:===
    + *
    + * Calculate the new means to be the centroids of the observations in the new clusters.
    + *
    + * `m^{(t+1)}_i = ( 1 / |S^{(t)}_i| ) \sum_{x_j \in S^{(t)}_i} x_j`
    + *
    + * Since the arithmetic mean is a least-squares estimator, this also minimizes the within-cluster
    + * sum of squares (WCSS) objective.
    + *
    + * @example
    + * {{{
    + *      val trainingDS: DataSet[Vector] = env.fromCollection(Clustering.trainingData)
    + *      val initialCentroids: DataSet[LabledVector] = env.fromCollection(Clustering.initCentroids)
    + *
    + *      val kmeans = KMeans()
    + *        .setInitialCentroids(initialCentroids)
    + *        .setNumIterations(10)
    + *
    + *      kmeans.fit(trainingDS)
    + *
    + *      // getting the computed centroids
    + *      val centroidsResult = kmeans.centroids.get.collect()
    + *
    + *      // get matching clusters for new points
    + *      val testDS: DataSet[Vector] = env.fromCollection(Clustering.testData)
    + *      val clusters: DataSet[LabeledVector] = kmeans.predict(testDS)
    + * }}}
    + *
    + * =Parameters=
    + *
    + * - [[org.apache.flink.ml.clustering.KMeans.NumIterations]]:
    + * Defines the number of iterations to recalculate the centroids of the clusters. As
it
    + * is a heuristic algorithm, there is no guarantee that it will converge to the global
optimum. The
    + * centroids of the clusters and the reassignment of the data points will be repeated
till the
    + * given number of iterations is reached.
    + * (Default value: '''10''')
    + *
    + * - [[org.apache.flink.ml.clustering.KMeans.InitialCentroids]]:
    + * Defines the initial k centroids of the k clusters. They are used as start off point
of the
    + * algorithm for clustering the data set. The centroids are recalculated as often as
set in
    + * [[org.apache.flink.ml.clustering.KMeans.NumIterations]]. The choice of the initial
centroids
    + * mainly affects the outcome of the algorithm.
    + *
    + */
    +class KMeans extends Predictor[KMeans] {
    +
    +  import KMeans._
    +
    +  /** Stores the learned clusters after the fit operation */
    +  var centroids: Option[DataSet[LabeledVector]] = None
    +
    +  /**
    +   * Sets the number of iterations.
    +   *
    +   * @param numIterations
    +   * @return itself
    +   */
    +  def setNumIterations(numIterations: Int): KMeans = {
    +    parameters.add(NumIterations, numIterations)
    +    this
    +  }
    +
    +  /**
    +   * Sets the initial centroids on which the algorithm will start computing.
    +   * These points should depend on the data and significantly influence the resulting
centroids.
    +   *
    +   * @param initialCentroids A sequence of labeled vectors.
    +   * @return itself
    +   */
    +  def setInitialCentroids(initialCentroids: DataSet[LabeledVector]): KMeans = {
    +    parameters.add(InitialCentroids, initialCentroids)
    +    this
    +  }
    +
    +}
    +
    +/**
    + * Companion object of KMeans. Contains convenience functions, the parameter type definitions
    + * of the algorithm and the [[FitOperation]] & [[PredictOperation]].
    + */
    +object KMeans {
    +  val CENTROIDS = "centroids"
    +
    +  case object NumIterations extends Parameter[Int] {
    +    val defaultValue = Some(10)
    +  }
    +
    +  case object InitialCentroids extends Parameter[DataSet[LabeledVector]] {
    +    val defaultValue = None
    +  }
    +
    +  // ========================================== Factory methods ====================================
    +
    +  def apply(): KMeans = {
    +    new KMeans()
    +  }
    +
    +  // ========================================== Operations =========================================
    +
    +  /**
    +   * [[PredictOperation]] for vector types. The result type is a [[LabeledVector]].
    +   */
    +  implicit def predictValues = {
    +    new PredictOperation[KMeans, Vector, LabeledVector] {
    +      override def predict(
    +        instance: KMeans,
    +        predictParameters: ParameterMap,
    +        input: DataSet[Vector])
    +      : DataSet[LabeledVector] = {
    +
    +        instance.centroids match {
    +          case Some(centroids) => {
    +            input.map(new SelectNearestCenterMapper).withBroadcastSet(centroids, CENTROIDS)
    +          }
    +
    +          case None => {
    +            throw new RuntimeException("The KMeans model has not been trained. Call first
fit" +
    +              "before calling the predict operation.")
    +          }
    +        }
    +      }
    +    }
    +  }
    +
    +  /**
    +   * [[FitOperation]] which iteratively computes centroids that match the given input
DataSet by
    +   * adjusting the given initial centroids.
    +   */
    +  implicit def fitKMeans = {
    +    new FitOperation[KMeans, Vector] {
    +      override def fit(
    +        instance: KMeans,
    +        fitParameters: ParameterMap,
    +        input: DataSet[Vector])
    +      : Unit = {
    +        val resultingParameters = instance.parameters ++ fitParameters
    +
    +        val centroids: DataSet[LabeledVector] = resultingParameters.get(InitialCentroids).get
    +        val numIterations: Int = resultingParameters.get(NumIterations).get
    +
    +        val finalCentroids = centroids.iterate(numIterations) { currentCentroids =>
    +          val newCentroids: DataSet[LabeledVector] = input
    +            .map(new SelectNearestCenterMapper).withBroadcastSet(currentCentroids, CENTROIDS)
    +            .map(x => (x.label, x.vector, 1.0)).withForwardedFields("label->_1;
vector->_2")
    +            .groupBy(x => x._1)
    +            .reduce((p1, p2) => (p1._1,(p1._2.asBreeze + p2._2.asBreeze).fromBreeze,
p1._3 + p2._3))
    +            .withForwardedFields("_1")
    +            .map(x => LabeledVector(x._1, (x._2.asBreeze :/ x._3).fromBreeze))
    +            .withForwardedFields("_1->label")
    +
    +          newCentroids
    +        }
    +
    +        instance.centroids = Some(finalCentroids)
    +      }
    +    }
    +  }
    +
    +}
    +
    +/**
    + * Converts a given vector into a labeled vector where the label denotes the label of
the closest
    + * centroid.
    + */
    +@ForwardedFields(Array("*->vector"))
    +final class SelectNearestCenterMapper extends RichMapFunction[Vector, LabeledVector]
{
    +
    +  import KMeans._
    +
    +  private var centroids: Traversable[LabeledVector] = null
    +
    +  /** Reads the centroid values from a broadcast variable into a collection. */
    +  override def open(parameters: Configuration) {
    +    centroids = getRuntimeContext.getBroadcastVariable[LabeledVector](CENTROIDS).asScala
    +  }
    +
    +  def map(v: Vector): LabeledVector = {
    +    var minDistance: Double = Double.MaxValue
    +    var closestCentroidLabel: Double = -1
    +    centroids.foreach(centroid => {
    +      val distance = EuclideanDistanceMetric().distance(v, centroid.vector)
    --- End diff --
    
    It would be nice to make the distance metric configurable by the user, so that metrics
other than Euclidean distance can be used,


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