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Fix issue with ward2 #494

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2 changes: 1 addition & 1 deletion R/mlClusteringHierarchical.R
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ mlClusteringHierarchical <- function(jaspResults, dataset, options, ...) {
if (options[["linkage"]] == "wardD") {
linkage <- "ward.D"
} else if (options[["linkage"]] == "wardD2") {
linkage <- "ward.D"
linkage <- "ward.D2"
} else {
linkage <- options[["linkage"]]
}
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68 changes: 34 additions & 34 deletions tests/testthat/test-mlclusteringhierarchical.R
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ options <- initMlOptions("mlClusteringHierarchical")
options$addPredictions <- FALSE
options$predictionsColumn <- ""
options$validationMeasures <- TRUE
options$linkage <- "wardD"
options$linkage <- "wardD2"
options$modelOptimization <- "optimized"
options$predictors <- c("Alcohol", "Malic", "Ash", "Alcalinity", "Magnesium", "Phenols", "Flavanoids", "Nonflavanoids", "Proanthocyanins", "Color", "Hue", "Dilution", "Proline")
options$predictors.types <- rep("scale", length(options$predictors))
Expand All @@ -172,56 +172,56 @@ options$tableClusterMeans <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("mlClusteringHierarchical", "wine.csv", options)

test_that("Evaluation Metrics table results match", {
test_that("Model Performance Metrics table results match", {
table <- results[["results"]][["clusterEvaluationMetrics"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Maximum diameter", 8.97000459794371, "Minimum separation", 1.65382813821881,
"Pearson's <unicode><unicode>", 0.576069991420004, "Dunn index",
0.184373165048091, "Entropy", 1.6642067641347, "Calinski-Harabasz index",
39.017310096389))
list("Maximum diameter", 8.65188420870295, "Minimum separation", 1.65382813821881,
"Pearson's <unicode>", 0.568236148438596, "Dunn index", 0.191152366158023,
"Entropy", 1.63956317716101, "Calinski-Harabasz index", 39.1289637915435
))
})

test_that("Cluster Information table results match", {
table <- results[["results"]][["clusterInfoTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.242534984865065, 56, 261.487531765994, 2, 0.109109073675993,
9, 117.635245012931, 3, 0.230765129303098, 36, 248.797937801422,
4, 0.202904096764332, 34, 218.759745022484, 5, 0.123616307170801,
23, 133.27612536436, 6, 0.0910704082207109, 20, 98.1869740392464
list(1, 0.268080239675824, 58, 288.590093334574, 2, 0.0399824529712903,
6, 43.0413664531293, 3, 0.241885028860911, 30, 260.390781281077,
4, 0.216632828426341, 36, 233.206625935916, 5, 0.142210541161857,
28, 153.090557501325, 6, 0.0912089089037752, 20, 98.1869740392464
))
})

test_that("Cluster Means table results match", {
table <- results[["results"]][["clusterMeansTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.781843207924736, 0.92528108110726, 0.259388633050896, 0.215037009410226,
0.752415021725891, 0.953961965305775, 0.444752190789042, 0.431937572019575,
-0.307951562262038, -0.600313943067432, 0.86635481402651, 0.519144294355176,
1.19360329436145, "Cluster 1", 0.800021509606689, -0.195110344255247,
0.806509041705175, -0.553225269607803, 0.686211357071248, 0.70153100492565,
0.925047041258243, 1.90074197012781, -0.552711437686052, -0.291662537479379,
0.604330654214653, 1.05254674333779, 0.00810138286916384, "Cluster 2",
-0.146542572260378, -0.82982447794628, -0.989694853778986, -0.788552397373411,
0.507805196178362, 0.266590155973554, 0.268797435339725, -0.674284826744811,
-0.464440878157909, -0.617532099990733, 0.243488671519124, 0.204205628662902,
-0.777138085789446, "Cluster 3", 0.299192917546764, -0.120317329484489,
0.0287774127101431, 0.178313085214061, -1.190459435479, -1.24951928741746,
-0.834245202930723, -0.134293424521656, 0.816264749488125, 0.861878951732851,
-1.0157378437394, -1.11512524607075, -0.401366333769037, "Cluster 4",
0.712359274776484, -1.16078628111573, 0.144235344247606, -1.03779952171632,
0.0833591124064994, -0.209508778429251, 0.643821364725174, -0.712506031006811,
-0.12321813836773, 0.854994624710662, -0.512525358967146, -0.0904475233946064,
-0.767174266522769, "Cluster 5", 0.765086807112473, 0.530140371524544,
0.477441248000354, 1.95657926535688, -1.40168846358879, -1.1015268520218,
-1.46759041083875, 0.196634357272983, 0.701028886969743, 0.475246926074739,
-0.819863385236042, -0.295092620065656, -0.382313118173636,
"Cluster 6"))
list(-0.814718951000308, 0.896746630031742, 0.188036161313956, 0.225350706617625,
0.747514865472435, 0.962345918273369, 0.453938607686343, 0.381452016581806,
-0.327160651969942, -0.620302837886437, 0.867053370206996, 0.561736591613942,
1.1611068462929, "Cluster 1", 1.59353832340386, 0.116258853404513,
1.93850225204976, -0.375172259495944, 0.988249857530258, 1.16706232279381,
0.929908149450233, 1.10333941218387, -0.335391806622396, -0.0684642069921506,
1.07568881611213, 0.48180924284713, -0.0748153887034146, "Cluster 2",
-0.328868448134948, -0.945954923754754, -1.12213198429158, -0.84160883941751,
0.458665253616639, 0.138555138801212, 0.442825108612933, -0.25496777432356,
-0.51233076482022, -0.692526739034442, 0.173450707350176, 0.402604855023938,
-0.659219851493464, "Cluster 3", 0.277664529455091, -0.177659982628153,
-0.00249147266452801, 0.115491991115137, -1.15888393847886,
-1.22789329816235, -0.793840815429072, -0.0966541933073729,
0.766622615430375, 0.831025064871381, -1.03787763111556, -1.09063080952508,
-0.389987330010673, "Cluster 4", 0.795030837821801, -1.01918778591077,
0.0595613112912381, -1.03072640112871, 0.239581146640469, -0.0264426995133515,
0.454908434690164, -0.76958415684055, -0.187907153438954, 0.633653969769103,
-0.292353113010822, -0.0851843527569232, -0.908317963003814,
"Cluster 5", 0.765086807112473, 0.530140371524552, 0.47744124800035,
1.95657926535688, -1.40168846358878, -1.1015268520218, -1.46759041083875,
0.196634357272983, 0.701028886969743, 0.475246926074739, -0.819863385236042,
-0.295092620065658, -0.382313118173636, "Cluster 6"))
})

test_that("Hierarchical Clustering table results match", {
test_that("Model Summary: Hierarchical Clustering table results match", {
table <- results[["results"]][["clusteringTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.19, 1234.14, 1482.32, 6, 0.53144565014931, 178))
list(0.18, 1232.51, 1480.69, 6, 0.532157149697841, 178))
})

# Median linkage
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