context("getTSPInstanceOverview and filterTSPInstances")
test_that("getTSPInstanceOverview works well", {
testdata = system.file("testdata", package = "netgen")
actual.files = list.files(testdata, pattern = ".tsp$")
# without file path
df = getTSPInstancesOverview(testdata)
expect_is(df, "data.frame")
expect_subset(c("dimension", "edge_weight_type"), choices = colnames(df))
expect_true(all(dim(df) > 0))
expect_equal(length(actual.files), nrow(df))
# check with file path
df = getTSPInstancesOverview(testdata, append.filename = TRUE)
expect_is(df, "data.frame")
expect_subset(c("dimension", "edge_weight_type", "file.path"), choices = colnames(df))
expect_equal(length(actual.files), nrow(df))
})
test_that("filterTSPInstances works as a filter", {
testdata = system.file("testdata", package = "netgen")
actual.files = list.files(testdata, pattern = ".tsp$")
n = length(actual.files)
# check for dimension
df = filterTSPInstances(testdata, expr = quote(dimension > 100))
expect_is(df, "data.frame")
expect_true(nrow(df) < n)
expect_true(all(df$dimension > 100))
# check for more complicated subset
df = filterTSPInstances(testdata,
expr = quote(dimension >= 100 & dimension <= 1000 & edge_weight_type == "EUC_2D"))
expect_is(df, "data.frame")
expect_true(nrow(df) < n)
expect_true(all(df$dimension >= 100 & df$dimension <= 1000))
expect_true(all(df$edge_weight_type == "EUC_2D"))
expect_true(all(is.logical(df$opt.tour.known)))
expect_true(all(is.logical(df$opt.length.known)))
# check if we get only tours with optimal values known (only a280.tsp in our testdata)
df = filterTSPInstances(testdata, expr = TRUE, opt.known = TRUE)
expect_is(df, "data.frame")
expect_equal(nrow(df), 1L)
# check for paths only
df = filterTSPInstances(testdata,
expr = quote(dimension > 100),
paths.only = TRUE)
expect_true(is.character(df))
expect_true(length(df) > 0)
})
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