tests/testthat/test-mlclassificationknn.R
In jasp-stats/jaspMachineLearning: Machine Learning Module for JASP
context("Machine Learning KNN Classification")
# Test fixed model #########################################################
options <- initMlOptions("mlClassificationKnn")
options$holdoutData <- "holdoutManual"
options$modelOptimization <- "manual"
options$modelValid <- "validationManual"
options$predictionsColumn <- ""
options$predictors <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
options$predictors.types <- rep("scale", 4)
options$savePath <- ""
options$setSeed <- TRUE
options$target <- "Species"
options$target.types <- "nominal"
options$testIndicatorColumn <- ""
options$testSetIndicatorVariable <- ""
options$dataSplitPlot <- FALSE
options$confusionTable <- FALSE
set.seed(1)
results <- jaspTools::runAnalysis("mlClassificationKnn", "iris.csv", options)
table <- results[["results"]][["classificationTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Euclidean", 30, 120, 3, 0.9, "rectangular"))
# Test optimized model #########################################################
options <- initMlOptions("mlClassificationKnn")
options$addPredictions <- FALSE
options$addIndicator <- FALSE
options$andrewsCurve <- TRUE
options$predictionsColumn <- ""
options$classProportionsTable <- TRUE
options$savePath <- ""
options$saveModel <- FALSE
options$holdoutData <- "holdoutManual"
options$modelOptimization <- "optimized"
options$modelValid <- "validationManual"
options$noOfFolds <- 5
options$errorVsKPlot <- TRUE
options$weightsPlot <- TRUE
options$predictors <- c("Alcohol", "Malic", "Ash", "Alcalinity", "Magnesium", "Phenols",
"Flavanoids", "Nonflavanoids", "Proanthocyanins", "Color",
"Hue", "Dilution", "Proline")
options$predictors.types <- rep("scale", length(options$predictors))
options$rocCurve <- TRUE
options$setSeed <- TRUE
options$target <- "Type"
options$target.types <- "nominal"
options$testDataManual <- 0.2
options$testIndicatorColumn <- ""
options$testSetIndicatorVariable <- ""
options$validationDataManual <- 0.2
options$validationMeasures <- TRUE
options$tableShap <- TRUE
options$fromIndex <- 1
options$toIndex <- 5
options$featureImportanceTable <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("mlClassificationKnn", "wine.csv", options)
test_that("Andrews Curves Plot matches", {
plotName <- results[["results"]][["andrewsCurve"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "andrews-curves-plot")
})
test_that("Class Proportions table results match", {
table <- results[["results"]][["classProportionsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.331460674157303, 1, 0.371428571428571, 0.324561403508772, 0.310344827586207,
0.398876404494382, 2, 0.4, 0.368421052631579, 0.517241379310345,
0.269662921348315, 3, 0.228571428571429, 0.307017543859649,
0.172413793103448))
})
test_that("K-Nearest Neighbors Classification table results match", {
table <- results[["results"]][["classificationTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Euclidean", 1, 35, 114, 29, 0.942857142857143, 0.896551724137931,
"rectangular"))
})
test_that("Confusion Matrix table results match", {
table <- results[["results"]][["confusionTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Observed", 1, 13, 0, 0, "", 2, 1, 12, 1, "", 3, 0, 0, 8))
})
test_that("Data Split plot matches", {
plotName <- results[["results"]][["plotDataSplit"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "data-split")
})
test_that("Classification Accuracy Plot matches", {
plotName <- results[["results"]][["errorVsKPlot"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "classification-accuracy-plot")
})
test_that("Rectangular Weight Function plot matches", {
plotName <- results[["results"]][["weightsPlot"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "rectangular-weight-function")
})
test_that("ROC Curves Plot matches", {
plotName <- results[["results"]][["rocCurve"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "roc-curves-plot")
})
test_that("Evaluation Metrics table results match", {
table <- results[["results"]][["validationMeasures"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.971428571428571, 0.977272727272727, 0.962962962962963, 0.0714285714285714,
0, 0, 0.0454545454545455, 1, 0.941468871691272, 1, 0.928571428571429,
1, 0.4, 13, 0.954545454545455, 6.5, 0.942857142857143, 0.928571428571429,
0.923076923076923, 0, 0.142857142857143, 0.0869565217391304,
0, 2, 0.884651736929383, 0.91304347826087, 1, 0.857142857142857,
0.342857142857143, 14, 1, 6, 0.971428571428571, 0.981481481481482,
0.941176470588235, 0.111111111111111, 0, 0, 0.037037037037037,
3, 0.925184888651615, 1, 0.888888888888889, 1, 0.257142857142857,
8, 0.962962962962963, 4, 0.961904761904762, 0.962441879108546,
0.942028777322895, 0.0608465608465608, 0.0476190476190476, 0.0289855072463768,
0.0274971941638608, "Average / Total", 0.91710183242409, 0.971014492753623,
0.948072562358277, 0.942857142857143, 1, 35, 0.972502805836139,
5.5
))
})
test_that("Additive Explanations for Predictions of Test Set Cases table results match", {
table <- results[["results"]][["tableShap"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.0175438596491228, 0.0614035087719298, 0.0350877192982456, 0.0789473684210527,
0.0526315789473685, 0.087719298245614, 0.219298245614035, 0.105263157894737,
-0.00877192982456138, 0.0175438596491229, 0.043859649122807,
0, 0, 0.324561403508772, 1, "1 (1)", 0, 0.18421052631579, 0.0350877192982456,
0.0175438596491229, 0, 0.149122807017544, 0, 0.0175438596491229,
0, 0.0964912280701754, 0.0614035087719298, -0.0175438596491229,
0.131578947368421, 0.324561403508772, 2, "1 (1)", 0.0526315789473684,
0.228070175438597, 0.0175438596491229, 0, 0, 0.0350877192982456,
0.140350877192982, 0, 0.0964912280701754, 0, 0.043859649122807,
0, 0.0614035087719298, 0.324561403508772, 3, "1 (1)", 0.0175438596491229,
0.0964912280701754, 0.0263157894736842, 0, 0, 0, 0.210526315789474,
0, 0.0701754385964912, 0, 0.00877192982456143, 0, 0.245614035087719,
0.324561403508772, 4, "1 (1)", -0.0175438596491229, 0.192982456140351,
0.0263157894736842, 0, 0, 0.175438596491228, 0, 0.0350877192982456,
0.00877192982456143, 0.0175438596491228, 0.0964912280701755,
0.00877192982456143, 0.131578947368421, 0.324561403508772, 5,
"1 (1)"))
})
# Tests specific for windows and linux
test_that("Feature Importance Metrics table results match", {
testthat::skip_on_os("mac")
table <- results[["results"]][["featureImportanceTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(35.3677070283885, "Proline", 12.7102697133271, "Alcohol", 11.7892356761295,
"Ash", 5.71041103062523, "Hue", 5.52620422318571, "Color", 4.42096337854857,
"Alcalinity", 4.23675657110904, "Malic", 2.76310211159285, "Dilution",
0.921034037197618, "Phenols", 0.921034037197618, "Nonflavanoids",
0.368413614879047, "Magnesium", 0.368413614879047, "Proanthocyanins",
0, "Flavanoids"))
})
# Tests specific for mac
test_that("Feature Importance Metrics table results match", {
testthat::skip_on_os(c("windows", "linux"))
table <- results[["results"]][["featureImportanceTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(35.3677070283885, "Proline", 12.7102697133271, "Alcohol", 11.7892356761295,
"Ash", 5.71041103062523, "Hue", 5.52620422318571, "Color", 4.42096337854857,
"Alcalinity", 4.23675657110904, "Malic", 2.76310211159285, "Dilution",
0.921034037197618, "Nonflavanoids", 0.921034037197618, "Phenols",
0.368413614879047, "Proanthocyanins", 0.368413614879047, "Magnesium",
0, "Flavanoids"))
})
jasp-stats/jaspMachineLearning documentation built on April 5, 2025, 3:52 p.m.
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context("Machine Learning KNN Classification")
# Test fixed model #########################################################
options <- initMlOptions("mlClassificationKnn")
options$holdoutData <- "holdoutManual"
options$modelOptimization <- "manual"
options$modelValid <- "validationManual"
options$predictionsColumn <- ""
options$predictors <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
options$predictors.types <- rep("scale", 4)
options$savePath <- ""
options$setSeed <- TRUE
options$target <- "Species"
options$target.types <- "nominal"
options$testIndicatorColumn <- ""
options$testSetIndicatorVariable <- ""
options$dataSplitPlot <- FALSE
options$confusionTable <- FALSE
set.seed(1)
results <- jaspTools::runAnalysis("mlClassificationKnn", "iris.csv", options)
table <- results[["results"]][["classificationTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Euclidean", 30, 120, 3, 0.9, "rectangular"))
# Test optimized model #########################################################
options <- initMlOptions("mlClassificationKnn")
options$addPredictions <- FALSE
options$addIndicator <- FALSE
options$andrewsCurve <- TRUE
options$predictionsColumn <- ""
options$classProportionsTable <- TRUE
options$savePath <- ""
options$saveModel <- FALSE
options$holdoutData <- "holdoutManual"
options$modelOptimization <- "optimized"
options$modelValid <- "validationManual"
options$noOfFolds <- 5
options$errorVsKPlot <- TRUE
options$weightsPlot <- TRUE
options$predictors <- c("Alcohol", "Malic", "Ash", "Alcalinity", "Magnesium", "Phenols",
"Flavanoids", "Nonflavanoids", "Proanthocyanins", "Color",
"Hue", "Dilution", "Proline")
options$predictors.types <- rep("scale", length(options$predictors))
options$rocCurve <- TRUE
options$setSeed <- TRUE
options$target <- "Type"
options$target.types <- "nominal"
options$testDataManual <- 0.2
options$testIndicatorColumn <- ""
options$testSetIndicatorVariable <- ""
options$validationDataManual <- 0.2
options$validationMeasures <- TRUE
options$tableShap <- TRUE
options$fromIndex <- 1
options$toIndex <- 5
options$featureImportanceTable <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("mlClassificationKnn", "wine.csv", options)
test_that("Andrews Curves Plot matches", {
plotName <- results[["results"]][["andrewsCurve"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "andrews-curves-plot")
})
test_that("Class Proportions table results match", {
table <- results[["results"]][["classProportionsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.331460674157303, 1, 0.371428571428571, 0.324561403508772, 0.310344827586207,
0.398876404494382, 2, 0.4, 0.368421052631579, 0.517241379310345,
0.269662921348315, 3, 0.228571428571429, 0.307017543859649,
0.172413793103448))
})
test_that("K-Nearest Neighbors Classification table results match", {
table <- results[["results"]][["classificationTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Euclidean", 1, 35, 114, 29, 0.942857142857143, 0.896551724137931,
"rectangular"))
})
test_that("Confusion Matrix table results match", {
table <- results[["results"]][["confusionTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Observed", 1, 13, 0, 0, "", 2, 1, 12, 1, "", 3, 0, 0, 8))
})
test_that("Data Split plot matches", {
plotName <- results[["results"]][["plotDataSplit"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "data-split")
})
test_that("Classification Accuracy Plot matches", {
plotName <- results[["results"]][["errorVsKPlot"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "classification-accuracy-plot")
})
test_that("Rectangular Weight Function plot matches", {
plotName <- results[["results"]][["weightsPlot"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "rectangular-weight-function")
})
test_that("ROC Curves Plot matches", {
plotName <- results[["results"]][["rocCurve"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "roc-curves-plot")
})
test_that("Evaluation Metrics table results match", {
table <- results[["results"]][["validationMeasures"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.971428571428571, 0.977272727272727, 0.962962962962963, 0.0714285714285714,
0, 0, 0.0454545454545455, 1, 0.941468871691272, 1, 0.928571428571429,
1, 0.4, 13, 0.954545454545455, 6.5, 0.942857142857143, 0.928571428571429,
0.923076923076923, 0, 0.142857142857143, 0.0869565217391304,
0, 2, 0.884651736929383, 0.91304347826087, 1, 0.857142857142857,
0.342857142857143, 14, 1, 6, 0.971428571428571, 0.981481481481482,
0.941176470588235, 0.111111111111111, 0, 0, 0.037037037037037,
3, 0.925184888651615, 1, 0.888888888888889, 1, 0.257142857142857,
8, 0.962962962962963, 4, 0.961904761904762, 0.962441879108546,
0.942028777322895, 0.0608465608465608, 0.0476190476190476, 0.0289855072463768,
0.0274971941638608, "Average / Total", 0.91710183242409, 0.971014492753623,
0.948072562358277, 0.942857142857143, 1, 35, 0.972502805836139,
5.5
))
})
test_that("Additive Explanations for Predictions of Test Set Cases table results match", {
table <- results[["results"]][["tableShap"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.0175438596491228, 0.0614035087719298, 0.0350877192982456, 0.0789473684210527,
0.0526315789473685, 0.087719298245614, 0.219298245614035, 0.105263157894737,
-0.00877192982456138, 0.0175438596491229, 0.043859649122807,
0, 0, 0.324561403508772, 1, "1 (1)", 0, 0.18421052631579, 0.0350877192982456,
0.0175438596491229, 0, 0.149122807017544, 0, 0.0175438596491229,
0, 0.0964912280701754, 0.0614035087719298, -0.0175438596491229,
0.131578947368421, 0.324561403508772, 2, "1 (1)", 0.0526315789473684,
0.228070175438597, 0.0175438596491229, 0, 0, 0.0350877192982456,
0.140350877192982, 0, 0.0964912280701754, 0, 0.043859649122807,
0, 0.0614035087719298, 0.324561403508772, 3, "1 (1)", 0.0175438596491229,
0.0964912280701754, 0.0263157894736842, 0, 0, 0, 0.210526315789474,
0, 0.0701754385964912, 0, 0.00877192982456143, 0, 0.245614035087719,
0.324561403508772, 4, "1 (1)", -0.0175438596491229, 0.192982456140351,
0.0263157894736842, 0, 0, 0.175438596491228, 0, 0.0350877192982456,
0.00877192982456143, 0.0175438596491228, 0.0964912280701755,
0.00877192982456143, 0.131578947368421, 0.324561403508772, 5,
"1 (1)"))
})
# Tests specific for windows and linux
test_that("Feature Importance Metrics table results match", {
testthat::skip_on_os("mac")
table <- results[["results"]][["featureImportanceTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(35.3677070283885, "Proline", 12.7102697133271, "Alcohol", 11.7892356761295,
"Ash", 5.71041103062523, "Hue", 5.52620422318571, "Color", 4.42096337854857,
"Alcalinity", 4.23675657110904, "Malic", 2.76310211159285, "Dilution",
0.921034037197618, "Phenols", 0.921034037197618, "Nonflavanoids",
0.368413614879047, "Magnesium", 0.368413614879047, "Proanthocyanins",
0, "Flavanoids"))
})
# Tests specific for mac
test_that("Feature Importance Metrics table results match", {
testthat::skip_on_os(c("windows", "linux"))
table <- results[["results"]][["featureImportanceTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(35.3677070283885, "Proline", 12.7102697133271, "Alcohol", 11.7892356761295,
"Ash", 5.71041103062523, "Hue", 5.52620422318571, "Color", 4.42096337854857,
"Alcalinity", 4.23675657110904, "Malic", 2.76310211159285, "Dilution",
0.921034037197618, "Nonflavanoids", 0.921034037197618, "Phenols",
0.368413614879047, "Proanthocyanins", 0.368413614879047, "Magnesium",
0, "Flavanoids"))
})
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