|
Github user skonto commented on a diff in the pull request:
https://github.com/apache/flink/pull/3192#discussion_r98570744
— Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/clustering/KMeans.scala —
@@ -0,0 +1,263 @@
+/*
+ * 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)
+ )
+ .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"))
— End diff –
All input is forwarded to vector? Shouldn't be the dataPoint? I might be wrong.
|
