[FLINK-1731] [ml] Implementation of Feature K-Means and Test Suite

flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.scala

@@ -0,0 +1,256 @@
+/*
+ * 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.java.functions.FunctionAnnotation.ForwardedFields
+import org.apache.flink.api.scala.{DataSet, _}
+import org.apache.flink.ml._
+import org.apache.flink.ml.common.{LabeledVector, _}
+import org.apache.flink.ml.math.Breeze._
+import org.apache.flink.ml.math.{BLAS, Vector}
+import org.apache.flink.ml.metrics.distances.EuclideanDistanceMetric
+import org.apache.flink.ml.pipeline._
+
+
+/**
+ * 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[Seq[LabeledVector]]] = None
+
+  /**
+   * Sets the maximum number of iterations.
+   *
+   * @param numIterations The maximum number of iterations.
+   * @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 set of labeled vectors.
+   * @return itself
+   */
+  def setInitialCentroids(initialCentroids: Seq[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 {
+
+  /** Euclidean Distance Metric */
+  val euclidean = EuclideanDistanceMetric()
+
+  case object NumIterations extends Parameter[Int] {
+    val defaultValue = Some(10)
+  }
+
+  case object InitialCentroids extends Parameter[Seq[LabeledVector]] {
+    val defaultValue = None
+  }
+
+  // ========================================== Factory methods ====================================
+
+  def apply(): KMeans = {
+    new KMeans()
+  }
+
+  // ========================================== Operations =========================================
+
+  /**
+   * [[PredictOperation]] for vector types. The result type is a [[LabeledVector]].
+   *
+   * @return Anew [[PredictDataSetOperation]] to predict the labels of a [[DataSet]] of [[Vector]]s.
+   * */
+  implicit def predictDataSet = {
+    new PredictDataSetOperation[KMeans, Vector, LabeledVector] {
+
+      /** Calculates the predictions for all elements in the [[DataSet]] input
+        *
+        * @param instance Reference to the current KMeans instance.
+        * @param predictParameters Container for predication parameter.
+        * @param testDS Data set to make predict on.
+        *
+        * @return A [[DataSet[LabeledVectors]] containing the nearest centroids.
+        */
+      override def predictDataSet(instance: KMeans, predictParameters: ParameterMap,
+                                  testDS: DataSet[Vector])
+      : DataSet[LabeledVector] = {
+        instance.centroids match {
+          case Some(centroids) =>
+            testDS.mapWithBcVariable(centroids)
+              { (dataPoint, centroids) => selectNearestCentroid(dataPoint, 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.
+   *
+   * @return A new  [[FitOperation]] to train the model using the training data set.
+   */
+  implicit def fitKMeans = {
+    new FitOperation[KMeans, Vector] {
+      override def fit(instance: KMeans, fitParameters: ParameterMap, trainingDS: DataSet[Vector])
+      : Unit = {
+        val resultingParameters = instance.parameters ++ fitParameters
+
+        val centroids: DataSet[Seq[LabeledVector]] = trainingDS
+          .getExecutionEnvironment
+          .fromElements(resultingParameters.get(InitialCentroids).get)
+        val numIterations: Int = resultingParameters.get(NumIterations).get
+
+        val finalCentroids = centroids.iterate(numIterations) { currentCentroids =>
+          val newCentroids: DataSet[LabeledVector] = trainingDS
+            .mapWithBcVariable(currentCentroids)
+              { (dataPoint, centroids) => selectNearestCentroid(dataPoint, 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))
+            // TODO replace addition of Breeze vectors by future build in flink function
+            .withForwardedFields("_1")
+            .map(x => {
+              BLAS.scal(1.0 / x._3, x._2)
+              LabeledVector(x._1, x._2)
+            })
+            .withForwardedFields("_1->label")
+
+          // currentCentroids contains only one element. So, this is output only once
+          currentCentroids.mapWithBcSet(newCentroids){
+            (_,newCenters) => newCenters
+          }
+        }
+
+        instance.centroids = Some(finalCentroids)
+      }
+    }
+  }
+
+  /**
+   * Converts a given vector into a labeled vector where the label denotes the label of the closest
+   * centroid.
+   *
+   * @param dataPoint The vector to determine the nearest centroid.
+   * @param centroids A collection of the centroids.
+   * @return A [[LabeledVector]] consisting of the input vector and the label of the closest
+   *         centroid.
+   */
+  @ForwardedFields(Array("*->vector"))
+  private def selectNearestCentroid(dataPoint: Vector, centroids: Seq[LabeledVector]) = {
+    var minDistance: Double = Double.MaxValue
+    var closestCentroidLabel: Double = -1
+    centroids.foreach(centroid => {
+      val distance = euclidean.distance(dataPoint, centroid.vector)
+      if (distance < minDistance) {
+        minDistance = distance
+        closestCentroidLabel = centroid.label
+      }
+    })
+    LabeledVector(closestCentroidLabel, dataPoint)
+  }
+
+}

flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/package.scala

@@ -18,12 +18,14 @@
 
 package org.apache.flink
 
+
 import org.apache.flink.api.common.functions.{RichFilterFunction, RichMapFunction}
 import org.apache.flink.api.common.typeinfo.TypeInformation
 import org.apache.flink.api.java.operators.DataSink
 import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment}
 import org.apache.flink.configuration.Configuration
 import org.apache.flink.ml.common.LabeledVector
+import scala.collection.JavaConverters._
 
 import scala.reflect.ClassTag
 
@@ -70,6 +72,14 @@ package object ml {
       dataSet.map(new BroadcastSingleElementMapperWithIteration[T, B, O](dataSet.clean(fun)))
         .withBroadcastSet(broadcastVariable, "broadcastVariable")
     }
+
+    def mapWithBcSet[B, O: TypeInformation: ClassTag](
+        broadcastVariable: DataSet[B])(
+        fun: (T, Seq[B]) => O)
+    : DataSet[O] = {
+      dataSet.map(new BroadcastSetMapper[T, B, O](dataSet.clean(fun)))
+        .withBroadcastSet(broadcastVariable, "broadcastVariable")
+    }
   }
 
   private class BroadcastSingleElementMapper[T, B, O](
@@ -102,6 +112,7 @@ package object ml {
     }
   }
 
+
   private class BroadcastSingleElementFilter[T, B](
       fun: (T, B) => Boolean)
     extends RichFilterFunction[T] {
@@ -116,4 +127,21 @@ package object ml {
       fun(value, broadcastVariable)
     }
   }
+
+  private class BroadcastSetMapper[T, B, O](fun: (T, Seq[B]) => O)
+    extends RichMapFunction[T, O] {
+    var broadcastVariable: Seq[B] = _
+
+    @throws(classOf[Exception])
+    override def open(configuration: Configuration): Unit = {
+      broadcastVariable = getRuntimeContext
+        .getBroadcastVariable[B]("broadcastVariable")
+        .asScala
+        .toSeq
+    }
+
+    override def map(value: T): O = {
+      fun(value, broadcastVariable)
+    }
+  }
 }