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test_that("classif_dcSVM", {
requirePackagesOrSkip("SwarmSVM", default.method = "load")
# Early Prediction
model = suppressMessages(SwarmSVM::dcSVM(
x = data.matrix(binaryclass.train[, -61]), y = binaryclass.train[, 61],
m = 100, k = 10, max.levels = 1, early = 1, seed = 0))
p = predict(model, data.matrix(binaryclass.test[, -61]))
p = factor(p, labels = levels(binaryclass.train[, 61]))
testSimple("classif.dcSVM", binaryclass.df, binaryclass.target, binaryclass.train.inds, p,
parset = list(m = 100, k = 10, max.levels = 1, early = 1, seed = 0))
# Exact Prediction
# suppressed Warning: "max.levels reduced"
model = suppressMessages(suppressWarnings(SwarmSVM::dcSVM(
x = data.matrix(binaryclass.train[, -61]),
y = binaryclass.train[, 61], m = 100, k = 2, max.levels = 3, early = 0, seed = 0)))
p = predict(model, data.matrix(binaryclass.test[, -61]))
p = factor(p, labels = levels(binaryclass.train[, 61]))
# suppressed Warning: "max.levels reduced"
suppressWarnings(
testSimple("classif.dcSVM", binaryclass.df, binaryclass.target, binaryclass.train.inds, p,
parset = list(m = 100, k = 2, max.levels = 3, early = 0, seed = 0))
)
# Prediction result containing only one class
data = data.frame(a = c(1, 2, 1, 2), b = c(1, 1, 2, 2), c = c("a", "b", "a", "b"))
traintask = makeClassifTask("train", data, "c")
testtask = makeClusterTask("test", data.frame(a = c(1, 1), b = c(1, 1)))
x = train(makeLearner("classif.dcSVM", seed = 0), traintask)
result = predict(x, testtask)$data$response
expect_equal(as.character(result), c("a", "a"))
})
test_that("classif_dcSVM works correctly when e1071 is used (#733)", {
requirePackagesOrSkip("SwarmSVM", default.method = "load")
expect_silent(train(makeLearner("classif.dcSVM", kernel = 1), binaryclass.task))
})
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