Nothing
library(ORION)
test_that("Full pipeline without data projection",{
### load file
file <- system.file("extdata", "artificial_1D_alternatives_sd_0.1-0.4.RData", package = "ORION")
#file <- system.file("extdata", "artificial_1D_linear_sd0.2.RData", package = "ORION")
load(file)
## project the data
projectedData <- projectData(dataset=dataset, comb=c(0,5))
## generate CV fold lists
foldList <- generateCVRuns(labels = dataset$labs,
ntimes = 10,
nfold = 10)
### test-predictionMap e1071 svm
predMap <- predictionMap(data=t(projectedData$data),
labels=projectedData$labs,
foldList = foldList,
parallel = FALSE,
classifier = tunePareto.svm(), kernel='linear')
## test subcascades - keep longest cascades
sub <- subcascades(predictionMap=predMap, sets = NULL, thresh=1, size=NA, numSol=1000)[1]
class(sub) <- "Subcascades"
sets <- rownames(sub[[1]])
simplifiedSub <- list()
while(!is.null(sub)){
head <- sub[1]
sets <- rownames(head[[1]])
simplifiedSub <- c(simplifiedSub, head)
sub <- dropSets(subcascades=sub, sets=sets, ordered = TRUE, neighborhood = "indirect", type = "any", direction="super")
}
class(simplifiedSub) <- "Subcascades"
### generate dot string graph
dot_str <-generateGraphs(subcascade=simplifiedSub, useOldLabels=F, categoricalLabels=NULL)
#g <- generateGraphs(subcascade=subArt1_0, useOldLabels=T, numericLabels=0:10, categoricalLabels=letters[1:10]) # with categorical lables
#dot(dot_str)
expect_type(dot_str, "character")
})
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