Nothing
context("Features: MISC - Nearest Better")
test_that("Expected Output", {
set.seed(2015*03*26)
# (1) create a feature object:
X = t(replicate(n = 2000L, expr = runif(n = 5L, min = -10, max = 10)))
y = apply(X, 1L, function(x) sum(x^2))
feat.object = createFeatureObject(X = X, y = y)
# (2) compute the nearest better features
features = calculateFeatureSet(feat.object, "nbc")
# test return value types and ranges
expect_identical(length(features), 7L)
expect_list(features)
expect_identical(as.character(sapply(features, class)), c(rep("numeric", 5L), "integer", "numeric"))
expect_true( testNumber(features$nbc.nn_nb.sd_ratio, lower = 0) )
expect_true( testNumber(features$nbc.nn_nb.mean_ratio) )
expect_true( testNumber(features$nbc.nn_nb.cor, lower = -1, upper = 1) )
expect_true( testNumber(features$nbc.dist_ratio.coeff_var) )
expect_true( testNumber(features$nbc.nb_fitness.cor, lower = -1, upper = 1) )
expect_identical(features$nbc.costs_fun_evals, 0L)
expect_true( testNumber(features$nbc.costs_runtime, lower = 0) )
})
test_that("Using Minkowski Distance", {
set.seed(2015*03*26)
# (1) create a feature object:
X = t(replicate(n = 2000L, expr = runif(n = 5L, min = -10, max = 10)))
y = apply(X, 1L, function(x) sum(x^2))
feat.object = createFeatureObject(X = X, y = y)
# (2) compute the nearest better features
features = calculateFeatureSet(feat.object, "nbc",
control = list(nbc.dist_method = "minkowski"))
features1 = calculateFeatureSet(feat.object, "nbc",
control = list(nbc.dist_method = "minkowski", nbc.minkowski_p = 10L))
features2 = calculateFeatureSet(feat.object, "nbc")
# test return value types and ranges
expect_list(features)
expect_list(features1)
expect_list(features2)
expect_identical(features[-7L], features2[-7L])
expect_false(identical(features[-7L], features1[-7L]))
expect_false(identical(features2[-7L], features1[-7L]))
})
test_that("Using Manhattan Distance", {
set.seed(2015*03*26)
# (1) create a feature object:
X = t(replicate(n = 2000L, expr = runif(n = 5L, min = -10, max = 10)))
y = apply(X, 1L, function(x) sum(x^2))
feat.object = createFeatureObject(X = X, y = y)
# (2) compute the nearest better features
features = calculateFeatureSet(feat.object, "nbc",
control = list(nbc.dist_method = "manhattan"))
# test return value types and ranges
expect_identical(length(features), 7L)
expect_list(features)
expect_identical(as.character(sapply(features, class)),
c(rep("numeric", 5L), "integer", "numeric"))
expect_true( testNumber(features$nbc.nn_nb.sd_ratio, lower = 0) )
expect_true( testNumber(features$nbc.nn_nb.mean_ratio) )
expect_true( testNumber(features$nbc.nn_nb.cor, lower = -1, upper = 1) )
expect_true( testNumber(features$nbc.dist_ratio.coeff_var) )
expect_true( testNumber(features$nbc.nb_fitness.cor, lower = -1, upper = 1) )
expect_identical(features$nbc.costs_fun_evals, 0L)
expect_true( testNumber(features$nbc.costs_runtime, lower = 0) )
})
test_that("Multiple Global Optima", {
set.seed(2015*03*26)
# (1) create a feature object:
X = t(replicate(n = 2000L, expr = runif(n = 5L, min = -10, max = 10)))
y = apply(X, 1L, function(x) sum(x^2))
y1 = y2 = y
y1[sample(seq_along(y), 2)] = min(y) - 10
y2[sample(seq_along(y), 3)] = min(y) - 10
feat.object1 = createFeatureObject(X = X, y = y)
feat.object2 = createFeatureObject(X = X, y = y1)
feat.object3 = createFeatureObject(X = X, y = y2)
# (2) compute the nearest better features
features1 = calculateFeatureSet(feat.object1, "nbc")
features2 = calculateFeatureSet(feat.object2, "nbc")
features3 = calculateFeatureSet(feat.object3, "nbc")
# test return value types and ranges
expect_equal(length(features1), 7L)
expect_list(features1)
expect_identical(as.character(sapply(features1, class)), c(rep("numeric", 5L), "integer", "numeric"))
expect_true( testNumber(features1$nbc.nn_nb.sd_ratio, lower = 0) )
expect_true( testNumber(features1$nbc.nn_nb.mean_ratio) )
expect_true( testNumber(features1$nbc.nn_nb.cor, lower = -1, upper = 1) )
expect_true( testNumber(features1$nbc.dist_ratio.coeff_var) )
expect_true( testNumber(features1$nbc.nb_fitness.cor, lower = -1, upper = 1) )
expect_identical(features1$nbc.costs_fun_evals, 0L)
expect_true( testNumber(features1$nbc.costs_runtime, lower = 0) )
expect_equal(length(features2), 7L)
expect_list(features2)
expect_identical(as.character(sapply(features2, class)), c(rep("numeric", 5L), "integer", "numeric"))
expect_true( testNumber(features2$nbc.nn_nb.sd_ratio, lower = 0) )
expect_true( testNumber(features2$nbc.nn_nb.mean_ratio) )
expect_true( testNumber(features2$nbc.nn_nb.cor, lower = -1, upper = 1) )
expect_true( testNumber(features2$nbc.dist_ratio.coeff_var) )
expect_true( testNumber(features2$nbc.nb_fitness.cor, lower = -1, upper = 1) )
expect_identical(features2$nbc.costs_fun_evals, 0L)
expect_true( testNumber(features2$nbc.costs_runtime, lower = 0) )
expect_equal(length(features3), 7L)
expect_list(features3)
expect_identical(as.character(sapply(features3, class)), c(rep("numeric", 5L), "integer", "numeric"))
expect_true( testNumber(features3$nbc.nn_nb.sd_ratio, lower = 0) )
expect_true( testNumber(features3$nbc.nn_nb.mean_ratio) )
expect_true( testNumber(features3$nbc.nn_nb.cor, lower = -1, upper = 1) )
expect_true( testNumber(features3$nbc.dist_ratio.coeff_var) )
expect_true( testNumber(features3$nbc.nb_fitness.cor, lower = -1, upper = 1) )
expect_identical(features3$nbc.costs_fun_evals, 0L)
expect_true( testNumber(features3$nbc.costs_runtime, lower = 0) )
})
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