test_that("bs instance works", {
rin = makeResampleInstance(makeResampleDesc("Bootstrap", iters = 3), size = 25)
iters = rin$desc$iters
expect_equal(iters, 3)
for (i in 1:iters) {
i1 = rin$train.inds[[i]]
i2 = rin$test.inds[[i]]
expect_equal(length(i1), 25)
expect_equal(length(i2), 25 - length(unique(i1)))
expect_true(min(i1) >= 1)
expect_true(max(i1) <= 25)
expect_true(min(i2) >= 1)
expect_true(max(i2) <= 25)
expect_equal(sort(c(unique(i1), i2)), 1:25)
}
})
test_that("bs resampling works", {
data = multiclass.df
formula = multiclass.formula
parset = list(minsplit = 12, cp = 0.09)
requirePackagesOrSkip("rpart", default.method = "load")
tt = function(formula, data, subset) {
rpart::rpart(formula, data = data[subset, ], minsplit = 12, cp = 0.09)
}
tp = function(model, newdata) {
predict(model, newdata, type = "class")
}
testBootstrap("classif.rpart", multiclass.df, multiclass.target,
tune.train = tt, tune.predict = tp, parset = parset)
})
test_that("bs instance is stochastic", {
rin = makeResampleInstance(makeResampleDesc("Bootstrap", iters = 3), size = 25)
iters = rin$desc$iters
expect_equal(iters, 3)
for (i in 1:iters) {
i1 = rin$train.inds[[i]]
i2 = rin$test.inds[[i]]
expect_equal(length(i1), 25)
expect_equal(length(i2), 25 - length(unique(i1)))
expect_true(min(i1) >= 1)
expect_true(max(i1) <= 25)
expect_true(min(i2) >= 1)
expect_true(max(i2) <= 25)
expect_equal(sort(c(unique(i1), i2)), 1:25)
}
rin1 = makeResampleInstance(makeResampleDesc("Bootstrap", iters = 3), size = 500)
rin2 = makeResampleInstance(makeResampleDesc("Bootstrap", iters = 3), size = 500)
expect_true(!all(sort(rin1$train.inds[[1]]) == sort(rin2$train.inds[[1]])))
})
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