context("fda_classif_shapelets")
test_that("fda_classif_shapelets", {
requirePackagesOrSkip("shapeletLib", default.method = "load")
set.seed(getOption("mlr.debug.seed"))
gp = getTaskData(gunpoint.task)
#gp = load2("../../demo4FDA/gunpoint.RData")
#df = as.data.frame(matrix(data = runif(1000), ncol = 100))
#df[,"X1"] = as.factor(sample(x = c(1,-1), replace = TRUE, size = 10))
m = shapeletLib::learnShapeletModel(data = gp[1:50,-1], label = as.factor(gp[1:50,1]))
p1 = predict(object = m, newdata = as.matrix(gp[51:200,-1]))
levs = c(2,1)
p1 = as.factor(ifelse(p1 > 0, levs[2L], levs[1L]))
lrn = makeLearner("fdaclassif.shapelet")
task = makeFDAClassifTask(data = gp, target = "X1", positive = "1")
m = try(train(lrn, task, subset = 1:50))
cp = predict(m, task, subset = 51:200)
expect_equal(as.character(cp$data$response), as.character(p1))
})
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