Nothing
data(satsolvers)
vbsp = sum(parscores(satsolvers, vbs))
vbsm = sum(misclassificationPenalties(satsolvers, vbs))
vbss = sum(successes(satsolvers, vbs))
test_that("singleBest and vbs", {
skip.expensive()
vbsse = sum(apply(satsolvers$data[satsolvers$success], 1, max))
expect_equal(vbsse, 2125)
expect_equal(vbss, 2125)
vbsp1 = sum(parscores(satsolvers, vbs, 1))
vbsp1e = sum(apply(satsolvers$data[satsolvers$performance], 1, min))
expect_equal(vbsp1e, 1288664.971)
expect_equal(vbsp1, 1288664.971)
expect_equal(vbsp, 11267864.97)
expect_equal(vbsm, 0)
sbp = sum(parscores(satsolvers, singleBest))
sbm = sum(misclassificationPenalties(satsolvers, singleBest))
sbs = sum(successes(satsolvers, singleBest))
sbse = sum(satsolvers$data[,"clasp_success"])
expect_equal(sbse, 2048)
expect_equal(sbs, 2048)
sbp1 = sum(parscores(satsolvers, singleBest, 1))
sbp1e = sum(satsolvers$data["clasp"])
expect_equal(sbp1e, 1586266.044)
expect_equal(sbp1, 1586266.044)
sbme = sum(apply(satsolvers$data[satsolvers$performance], 1, function(x) { abs(x["clasp"] - min(x)) }))
expect_equal(sbme, 297601.073)
expect_equal(sbm, 297601.073)
expect_equal(sbp, 14060266.04)
})
folds = cvFolds(satsolvers)
test_that("classify", {
skip.expensive()
res = classify(classifier=makeLearner("classif.OneR"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classify(classifier=list(makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
makeLearner("classif.OneR")),
data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classify(classifier=list(makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
makeLearner("classif.OneR"),
.combine=makeLearner("classif.OneR")),
data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("classifyPairs", {
skip.expensive()
res = classifyPairs(classifier=makeLearner("classif.OneR"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = classifyPairs(classifier=makeLearner("classif.OneR"),
data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("cluster", {
skip.expensive()
res = cluster(clusterer=makeLearner("cluster.SimpleKMeans"), data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=makeLearner("cluster.SimpleKMeans"), data=folds,
bestBy="successes", pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=list(makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans")),
data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = cluster(clusterer=list(makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
makeLearner("cluster.SimpleKMeans"),
.combine=makeLearner("classif.OneR")), data=folds, pre=normalize)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("regression", {
skip.expensive()
res = regression(regressor=makeLearner("regr.lm"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regression(regressor=makeLearner("regr.lm"),
data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regression(regressor=makeLearner("regr.lm"),
data=folds,
combine=makeLearner("classif.OneR"),
expand=function(x) { cbind(x, combn(c(1:ncol(x)), 2,
function(y) { abs(x[,y[1]] - x[,y[2]]) })) })
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("regressionPairs", {
skip.expensive()
res = regressionPairs(regressor=makeLearner("regr.lm"), data=folds)
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
res = regressionPairs(regressor=makeLearner("regr.lm"), data=folds,
combine=makeLearner("classif.OneR"))
expect_true(sum(parscores(folds, res)) > vbsp)
expect_true(sum(misclassificationPenalties(folds, res)) > vbsm)
expect_true(sum(successes(folds, res)) < vbss)
expect_true(is.data.frame(res$predictor(satsolvers$data[satsolvers$features])))
})
test_that("perfScatterPlot", {
skip.expensive()
model = classify(classifier=makeLearner("classif.J48"), data=folds)
library(ggplot2)
p = perfScatterPlot(parscores, model, singleBest, folds, satsolvers) +
scale_x_log10() + scale_y_log10() +
xlab("J48") + ylab("single best")
expect_false(is.null(p))
satsolvers$extra = c("foo")
satsolvers$data$foo = 1:nrow(satsolvers$data)
p = perfScatterPlot(parscores, model, singleBest, folds, satsolvers, pargs=aes(colour = foo)) +
scale_x_log10() + scale_y_log10() +
xlab("J48") + ylab("single best")
expect_false(is.null(p))
})
test_that("tune", {
skip.expensive()
ps = makeParamSet(makeIntegerParam("M", lower = 1, upper = 100))
design = generateRandomDesign(10, ps)
res = tuneModel(folds, classify, makeLearner("classif.J48"), design, parscores, nfolds = 3L, quiet = TRUE)
expect_equal(class(res), "llama.model")
expect_equal(attr(res, "type"), "classify")
expect_true(res$parvals$M >= 1 && res$parvals$M <= 100)
expect_true(all(sapply(res$inner.parvals, function(x) (x$M >= 1 && x$M <= 100))))
})
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