Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/18538#discussion_r133370279
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala
---
@@ -0,0 +1,240 @@
+/*
+ * 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.spark.ml.evaluation
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.linalg.{BLAS, DenseVector, Vector, Vectors,
VectorUDT}
+import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol}
+import org.apache.spark.ml.util.{DefaultParamsReadable,
DefaultParamsWritable, Identifiable, SchemaUtils}
+import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.functions.{avg, col, udf}
+import org.apache.spark.sql.types.IntegerType
+
+/**
+ * Evaluator for clustering results.
+ * At the moment, the supported metrics are:
+ * squaredSilhouette: silhouette measure using the squared Euclidean
distance;
+ * cosineSilhouette: silhouette measure using the cosine distance.
+ * The implementation follows the proposal explained
+ * <a
href="https://drive.google.com/file/d/0B0Hyo%5f%5fbG%5f3fdkNvSVNYX2E3ZU0/view";>
+ * in this document</a>.
+ */
+@Experimental
+class ClusteringEvaluator (val uid: String)
+ extends Evaluator with HasPredictionCol with HasFeaturesCol with
DefaultParamsWritable {
+
+ def this() = this(Identifiable.randomUID("SquaredEuclideanSilhouette"))
+
+ override def copy(pMap: ParamMap): ClusteringEvaluator =
this.defaultCopy(pMap)
+
+ override def isLargerBetter: Boolean = true
+
+ /** @group setParam */
+ def setPredictionCol(value: String): this.type = set(predictionCol,
value)
+
+ /** @group setParam */
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /**
+ * param for metric name in evaluation
+ * (supports `"squaredSilhouette"` (default))
+ * @group param
+ */
+ val metricName: Param[String] = {
+ val allowedParams = ParamValidators.inArray(Array("squaredSilhouette"))
+ new Param(
+ this,
+ "metricName",
+ "metric name in evaluation (squaredSilhouette)",
+ allowedParams
+ )
+ }
+
+ /** @group getParam */
+ def getMetricName: String = $(metricName)
+
+ /** @group setParam */
+ def setMetricName(value: String): this.type = set(metricName, value)
+
+ setDefault(metricName -> "squaredSilhouette")
+
+ override def evaluate(dataset: Dataset[_]): Double = {
+ SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new
VectorUDT)
+ SchemaUtils.checkColumnType(dataset.schema, $(predictionCol),
IntegerType)
+
+ val metric: Double = $(metricName) match {
+ case "squaredSilhouette" =>
+ SquaredEuclideanSilhouette.computeSquaredSilhouette(
+ dataset,
+ $(predictionCol),
+ $(featuresCol)
+ )
+ }
+ metric
+ }
+
+}
+
+
+object ClusteringEvaluator
+ extends DefaultParamsReadable[ClusteringEvaluator] {
+
+ override def load(path: String): ClusteringEvaluator = super.load(path)
+
+}
+
+private[evaluation] object SquaredEuclideanSilhouette {
+
+ private[this] var kryoRegistrationPerformed: Boolean = false
+
+ /**
+ * This method registers the class
+ *
[[org.apache.spark.ml.evaluation.SquaredEuclideanSilhouette.ClusterStats]]
+ * for kryo serialization.
+ *
+ * @param sc `SparkContext` to be used
+ */
+ def registerKryoClasses(sc: SparkContext): Unit = {
+ if (! kryoRegistrationPerformed) {
+ sc.getConf.registerKryoClasses(
+ Array(
+ classOf[SquaredEuclideanSilhouette.ClusterStats]
+ )
+ )
+ kryoRegistrationPerformed = true
+ }
+ }
+
+ case class ClusterStats(featureSum: Vector, squaredNormSum: Double,
numOfPoints: Long)
+
+ def computeClusterStats(
+ df: DataFrame,
+ predictionCol: String,
+ featuresCol: String): Map[Int, ClusterStats] = {
+ val numFeatures =
df.select(col(featuresCol)).first().getAs[Vector](0).size
+ val clustersStatsRDD = df.select(col(predictionCol), col(featuresCol),
col("squaredNorm"))
+ .rdd
+ .map { row => (row.getInt(0), (row.getAs[Vector](1),
row.getDouble(2))) }
+ .aggregateByKey[(DenseVector, Double,
Long)]((Vectors.zeros(numFeatures).toDense, 0.0, 0L))(
+ seqOp = {
+ case (
+ (featureSum: DenseVector, squaredNormSum: Double,
numOfPoints: Long),
+ (features, squaredNorm)
+ ) =>
+ BLAS.axpy(1.0, features, featureSum)
+ (featureSum, squaredNormSum + squaredNorm, numOfPoints + 1)
+ },
+ combOp = {
+ case (
+ (featureSum1, squaredNormSum1, numOfPoints1),
+ (featureSum2, squaredNormSum2, numOfPoints2)
+ ) =>
+ BLAS.axpy(1.0, featureSum2, featureSum1)
+ (featureSum1, squaredNormSum1 + squaredNormSum2, numOfPoints1
+ numOfPoints2)
+ }
+ )
+
+ clustersStatsRDD
+ .collectAsMap()
+ .mapValues {
+ case (featureSum: DenseVector, squaredNormSum: Double,
numOfPoints: Long) =>
+ SquaredEuclideanSilhouette.ClusterStats(featureSum,
squaredNormSum, numOfPoints)
+ }
+ .toMap
+ }
+
+ def computeSquaredSilhouetteCoefficient(
+ broadcastedClustersMap: Broadcast[Map[Int, ClusterStats]],
+ vector: Vector,
+ clusterId: Int,
+ squaredNorm: Double): Double = {
+
+ def compute(squaredNorm: Double, point: Vector, clusterStats:
ClusterStats): Double = {
+ val pointDotClusterFeaturesSum = BLAS.dot(point,
clusterStats.featureSum)
+
+ squaredNorm +
+ clusterStats.squaredNormSum / clusterStats.numOfPoints -
+ 2 * pointDotClusterFeaturesSum / clusterStats.numOfPoints
+ }
+
+ var minOther = Double.MaxValue
+ for(c <- broadcastedClustersMap.value.keySet) {
+ if (c != clusterId) {
+ val sil = compute(squaredNorm, vector,
broadcastedClustersMap.value(c))
+ if(sil < minOther) {
+ minOther = sil
+ }
+ }
+ }
+ val clusterCurrentPoint = broadcastedClustersMap.value(clusterId)
+ // adjustment for excluding the node itself from
+ // the computation of the average dissimilarity
+ val clusterSil = if (clusterCurrentPoint.numOfPoints == 1) {
+ 0
+ } else {
+ compute(squaredNorm, vector, clusterCurrentPoint) *
clusterCurrentPoint.numOfPoints /
+ (clusterCurrentPoint.numOfPoints - 1)
+ }
+
+ var silhouetteCoeff = 0.0
+ if (clusterSil < minOther) {
+ silhouetteCoeff = 1 - (clusterSil / minOther)
+ } else {
+ if (clusterSil > minOther) {
+ silhouetteCoeff = (minOther / clusterSil) - 1
+ }
+ }
+ silhouetteCoeff
+
--- End diff --
remove empty line
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