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new 3572c81 initial draft
3572c81 is described below
commit 3572c81466d500574c5f207d22c8e60bda5bc905
Author: Paul King <[email protected]>
AuthorDate: Thu Apr 17 23:19:46 2025 +1000
initial draft
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+= A first look at Underdog
+Paul King
+:revdate: 2025-04-17T22:30:00+00:00
+:draft: true
+:keywords: whisky, groovy, kmeans, clustering
+:description: This post looks at using the Underdog data science library.
+
+++++
+<table><tr><td style="padding: 0px; padding-left: 20px; padding-right: 20px;
font-size: 18pt; line-height: 1.5; margin: 0px">
+++++
+[blue]#_Let's explore Whisky profiles using Underdog!_#
+++++
+</td></tr></table>
+++++
+
+A relatively new data science library is
+https://grooviter.github.io/underdog/[Underdog].
+Let's use it to explore Whiskey profiles.
+It has many Groovy-powered features delivering a very expressive developer
experience.
+
+Underdog sits on top of some well-known data-science libraries like Smile and
Tablesaw, so
+if you have used either of those libraries, you'll recognise parts of the
functionality.
+
+First, we'll load our CSV file:
+
+[source,groovy]
+----
+def file = new File(getClass().classLoader.getResource('whiskey.csv').file)
+def df = Underdog.df().read_csv(file.path).drop('RowID')
+----
+
+Let's look at the shape of and schema for the data:
+
+[source,groovy]
+----
+println df.shape()
+println df.schema()
+----
+
+It gives this output:
+
+----
+86 rows X 13 cols
+ Structure of whiskey.csv
+ Index | Column Name | Column Type |
+-----------------------------------------
+ 0 | Distillery | STRING |
+ 1 | Body | INTEGER |
+ 2 | Sweetness | INTEGER |
+ 3 | Smoky | INTEGER |
+ 4 | Medicinal | INTEGER |
+ 5 | Tobacco | INTEGER |
+ 6 | Honey | INTEGER |
+ 7 | Spicy | INTEGER |
+ 8 | Winey | INTEGER |
+ 9 | Nutty | INTEGER |
+ 10 | Malty | INTEGER |
+ 11 | Fruity | INTEGER |
+ 12 | Floral | INTEGER |
+----
+
+Let's look at a correlation matrix plot of the data:
+
+[source,groovy]
+----
+def plot = Underdog.plots()
+def features = df.columns - 'Distillery'
+plot.correlationMatrix(df[features]).show()
+----
+
+Which has this output:
+
+image:img/underdogCorrelationPlot.png[correlation plot,50%]
+
+We can also look at the data for any individual distillery using a radar plot.
Let's look at it for row 0:
+
+[source,groovy]
+----
+def data = df[features] as double[][]
+plot.radar(
+ features,
+ [4] * features.size(),
+ data[0].toList(),
+ df['Distillery'][0]
+).show()
+----
+
+Which has this output:
+
+image:img/underdogCorrelationPlot.png[radar plot,50%]
+
+Let's now cluster the distilleries using k-means:
+
+[source,groovy]
+----
+def ml = Underdog.ml()
+def clusters = ml.clustering.kMeans(data, nClusters: 3)
+df['Cluster'] = clusters.toList()
+
+println 'Clusters'
+for (int i in clusters.toSet()) {
+ println "$i:${df[df['Cluster'] == i]['Distillery'].join(', ')}"
+}
+----
+
+It gives the following output:
+
+----
+Clusters
+0:Aberfeldy, Aberlour, Auchroisk, Balmenach, Belvenie, BenNevis, Benrinnes,
Benromach, BlairAthol, Dailuaine, Dalmore, Edradour, GlenOrd, Glendronach,
Glendullan, Glenfarclas, Glenlivet, Glenrothes, Glenturret, Knochando,
Longmorn, Macallan, Mortlach, RoyalLochnagar, Strathisla
+1:Ardbeg, Balblair, Bowmore, Bruichladdich, Caol Ila, Clynelish, GlenGarioch,
GlenScotia, Highland Park, Isle of Jura, Lagavulin, Laphroig, Oban,
OldPulteney, Springbank, Talisker, Teaninich
+2:AnCnoc, Ardmore, ArranIsleOf, Auchentoshan, Aultmore, Benriach, Bladnoch,
Bunnahabhain, Cardhu, Craigallechie, Craigganmore, Dalwhinnie, Deanston,
Dufftown, GlenDeveronMacduff, GlenElgin, GlenGrant, GlenKeith, GlenMoray,
GlenSpey, Glenallachie, Glenfiddich, Glengoyne, Glenkinchie, Glenlossie,
Glenmorangie, Inchgower, Linkwood, Loch Lomond, Mannochmore, Miltonduff,
OldFettercairn, RoyalBrackla, Scapa, Speyburn, Speyside, Strathmill, Tamdhu,
Tamnavulin, Tobermory, Tomatin, Tomintoul, Tom [...]
+----
+
+Finally, let's project our data onto 2 dimensions using PCA and plot that as a
scatter plot:
+
+[source,groovy]
+----
+def pca = ml.features.pca(data, 2)
+def projected = pca.apply(data)
+
+df['X'] = projected*.getAt(0)
+df['Y'] = projected*.getAt(1)
+
+plot.scatter(
+ df['X'],
+ df['Y'],
+ df['Cluster'],
+ 'Whiskey Clusters'
+).show()
+----
+
+The output looks like this:
+
+image:img/underdogScatterPlot.png[scatter plot,50%]
+
+== Further information
+
+* https://grooviter.github.io/underdog/[Underdog]
+
+== Conclusion
+
+We have looked at how to use Underdog.
+
+.Update history
+****
+*17/Apr/2025*: Initial version +
+****
