minor refactor (show cluster allocations)

subprojects/WhiskeyWayang/src/main/groovy/WhiskeyWayang.groovy

@@ -15,7 +15,9 @@
  */
 
 // inspired by the Apache Wayang Scala example here:
-// https://github.com/apache/incubator-wayang/blob/main/README.md#k-means
+// https://github.com/apache/incubator-wayang/blob/011859f8bd2b8f05a4ffc2d71c73edf1dd893fac/README.md#k-means
+// See also a way to resurrect "lost" (no points selected) centroids:
+// https://github.com/apache/incubator-wayang/blob/9071f6c3d47611657407a9287923294cb2b07e8c/wayang-benchmark/src/main/scala/org/apache/wayang/apps/kmeans/Kmeans.scala#L79
 
 import org.apache.wayang.api.JavaPlanBuilder
 import org.apache.wayang.core.api.WayangContext
@@ -27,10 +29,7 @@ import org.apache.wayang.java.Java
 import org.apache.wayang.spark.Spark
 import static java.lang.Math.sqrt
 
-record Point(double[] pts) implements Serializable {
-    static Point fromLine(String line) {
-        new Point(line.split(',')[2..-1] as double[]) }
-}
+record Point(double[] pts) implements Serializable { }
 
 record PointGrouping(double[] pts, int cluster, long count) implements Serializable {
     PointGrouping(List<Double> pts, int cluster, long count) {
@@ -43,7 +42,7 @@ record PointGrouping(double[] pts, int cluster, long count) implements Serializa
     }
 
     PointGrouping average() {
-        new PointGrouping(pts.collect{ double d -> d/count }, cluster, count)
+        new PointGrouping(pts.collect{ double d -> d/count }, cluster, 1)
     }
 }
 
@@ -80,7 +79,9 @@ int iterations = 10
 
 // read in data from our file
 var url = WhiskeyWayang.classLoader.getResource('whiskey.csv').file
-var pointsData = new File(url).readLines()[1..-1].collect{ Point.fromLine(it) }
+def rows = new File(url).readLines()[1..-1]*.split(',')
+var distilleries = rows*.getAt(1)
+var pointsData = rows.collect{ new Point(it[2..-1] as double[]) }
 var dims = pointsData[0].pts.size()
 
 // create some random points as initial centroids
@@ -106,17 +107,30 @@ var finalCentroids = initialCentroids.repeat(iterations, currentCentroids ->
         .reduceByKey(cluster, plus).withName('Aggregate points')
         .map(average).withName('Average points')
         .withOutputClass(PointGrouping)
-).withName('Loop')
+).withName('Loop').collect()
 
 println 'Centroids:'
-finalCentroids.forEach { c ->
-    var pts = c.pts.collect { sprintf '%.2f', it }.join(', ')
-    println "Cluster$c.cluster ($c.count points): $pts"
+finalCentroids.each { c ->
+    println "Cluster $c.cluster: ${c.pts.collect { sprintf '%.2f', it }.join(', ')}"
 }
+
+println()
+var allocator = new SelectNearestCentroid(centroids: finalCentroids)
+var allocations = pointsData.withIndex()
+    .collect{ pt, idx -> [allocator.apply(pt).cluster, distilleries[idx]] }
+    .groupBy{ cluster, ds -> "Cluster $cluster" }
+    .collectValues{ v -> v.collect{ it[1] } }
+    .sort{ e1, e2 -> e1.key <=> e2.key }
+allocations.each{ c, ds -> println "$c (${ds.size()} members): ${ds.join(', ')}" }
 /*
 Centroids:
-Cluster0 (20 points): 2.00, 2.50, 1.55, 0.35, 0.20, 1.15, 1.55, 0.95, 0.90, 1.80, 1.35, 1.35
-Cluster2 (21 points): 2.81, 2.43, 1.52, 0.05, 0.00, 1.90, 1.67, 2.05, 2.10, 2.10, 2.19, 1.76
-Cluster3 (34 points): 1.38, 2.32, 1.09, 0.26, 0.03, 1.15, 1.09, 0.47, 1.38, 1.74, 2.03, 2.24
-Cluster4 (11 points): 2.91, 1.55, 2.91, 2.73, 0.45, 0.45, 1.45, 0.55, 1.55, 1.45, 1.18, 0.55
+Cluster 0: 2.53, 1.65, 2.76, 2.12, 0.29, 0.65, 1.65, 0.59, 1.35, 1.41, 1.35, 0.94
+Cluster 2: 3.33, 2.56, 1.67, 0.11, 0.00, 1.89, 1.89, 2.78, 2.00, 1.89, 2.33, 1.33
+Cluster 3: 1.42, 2.47, 1.03, 0.22, 0.06, 1.00, 1.03, 0.47, 1.19, 1.72, 1.92, 2.08
+Cluster 4: 2.25, 2.38, 1.38, 0.08, 0.13, 1.79, 1.54, 1.33, 1.75, 2.17, 1.75, 1.79
+
+Cluster 0 (17 members): Ardbeg, Balblair, Bowmore, Bruichladdich, Caol Ila, Clynelish, GlenGarioch, GlenScotia, Highland Park, Isle of Jura, Lagavulin, Laphroig, Oban, OldPulteney, Springbank, Talisker, Teaninich
+Cluster 2 (9 members): Aberlour, Balmenach, Dailuaine, Dalmore, Glendronach, Glenfarclas, Macallan, Mortlach, RoyalLochnagar
+Cluster 3 (36 members): AnCnoc, ArranIsleOf, Auchentoshan, Aultmore, Benriach, Bladnoch, Bunnahabhain, Cardhu, Craigganmore, Dalwhinnie, Dufftown, GlenElgin, GlenGrant, GlenMoray, GlenSpey, Glenallachie, Glenfiddich, Glengoyne, Glenkinchie, Glenlossie, Glenmorangie, Inchgower, Linkwood, Loch Lomond, Mannochmore, Miltonduff, RoyalBrackla, Speyburn, Speyside, Strathmill, Tamdhu, Tamnavulin, Tobermory, Tomintoul, Tomore, Tullibardine
+Cluster 4 (24 members): Aberfeldy, Ardmore, Auchroisk, Belvenie, BenNevis, Benrinnes, Benromach, BlairAthol, Craigallechie, Deanston, Edradour, GlenDeveronMacduff, GlenKeith, GlenOrd, Glendullan, Glenlivet, Glenrothes, Glenturret, Knochando, Longmorn, OldFettercairn, Scapa, Strathisla, Tomatin
 */