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test_that("tuneIrace", {
rdesc = makeResampleDesc("Holdout", stratify = TRUE, split = 0.1)
ps1 = makeParamSet(
makeNumericParam("cp", lower = 0.001, upper = 1),
makeIntegerParam("minsplit", lower = 1, upper = 10)
)
n = 100
ctrl = makeTuneControlIrace(
maxExperiments = n, nbIterations = 1L,
minNbSurvival = 1)
tr1 = tuneParams(makeLearner("classif.rpart"), multiclass.task, rdesc,
par.set = ps1, control = ctrl)
expect_true(getOptPathLength(tr1$opt.path) >= 10 &&
getOptPathLength(tr1$opt.path) <= n)
expect_number(tr1$y, lower = 0, upper = 0.3)
# with trafo
ps2 = makeParamSet(
makeNumericParam("C", lower = -5, upper = 5, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -5, upper = 5, trafo = function(x) 2^x)
)
n = 20
ctrl = makeTuneControlIrace(
maxExperiments = n, nbIterations = 1L,
minNbSurvival = 1)
tr2 = tuneParams(makeLearner("classif.ksvm"), multiclass.task, rdesc,
par.set = ps2, control = ctrl, measures = acc)
expect_true(getOptPathLength(tr2$opt.path) >= 10 &&
getOptPathLength(tr2$opt.path) <= n)
expect_number(tr2$y, lower = 0.8, upper = 1)
})
test_that("tuneIrace works with dependent params", {
ps = makeParamSet(
makeDiscreteParam("kernel", values = c("vanilladot", "rbfdot")),
makeNumericParam("sigma",
lower = 1, upper = 2,
requires = quote(kernel == "rbfdot"))
)
lrn = makeLearner("classif.ksvm")
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlIrace(
maxExperiments = 40, nbIterations = 2L,
minNbSurvival = 1)
tr = tuneParams(lrn, multiclass.task, rdesc, par.set = ps, control = ctrl)
expect_true(getOptPathLength(tr$opt.path) >= 20 &&
getOptPathLength(tr$opt.path) <= 100)
expect_true(!is.na(tr$y))
# another complex example
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeDiscreteParam("kernel", values = c("vanilladot", "polydot", "rbfdot")),
makeNumericParam("sigma",
lower = -12, upper = 12, trafo = function(x) 2^x,
requires = quote(kernel == "rbfdot")),
makeIntegerParam("degree",
lower = 2L, upper = 5L,
requires = quote(kernel == "polydot"))
)
ctrl = makeTuneControlRandom(maxit = 5L)
rdesc = makeResampleDesc("Holdout")
res = tuneParams("classif.ksvm", sonar.task, rdesc,
par.set = ps,
control = ctrl)
})
# we had a bug here
test_that("tuneIrace works with logical params", {
ps = makeParamSet(
makeLogicalParam("scaled"),
makeLogicalParam("shrinking")
)
lrn = makeLearner("classif.ksvm", kernel = "vanilladot")
rdesc = makeResampleDesc("Holdout", split = 0.3, stratify = TRUE)
ctrl = makeTuneControlIrace(
maxExperiments = 20, nbIterations = 1,
minNbSurvival = 1)
task = subsetTask(multiclass.task, c(1:10, 50:60, 100:110))
tr = tuneParams(lrn, task, rdesc, par.set = ps, control = ctrl)
expect_true(getOptPathLength(tr$opt.path) >= 15 &&
getOptPathLength(tr$opt.path) <= 20)
expect_true(!is.na(tr$y))
lrn2 = makeTuneWrapper(lrn, rdesc, par.set = ps, control = ctrl)
z = holdout(lrn2, task, split = 0.5, stratify = TRUE)
expect_true(getOptPathLength(tr$opt.path) >= 15 &&
getOptPathLength(tr$opt.path) <= 20)
expect_true(!is.na(tr$y))
})
test_that("tuneIrace works with tune.threshold", {
rdesc = makeResampleDesc("Holdout", stratify = TRUE, split = 0.1)
ps = makeParamSet(makeIntegerParam("minsplit", lower = 1, upper = 3))
n = 20
ctrl = makeTuneControlIrace(
maxExperiments = n, nbIterations = 1L,
minNbSurvival = 1)
expect_silent(tuneParams("classif.rpart", multiclass.task, rdesc,
par.set = ps,
control = ctrl))
})
test_that("tuneIrace uses digits", {
rdesc = makeResampleDesc(method = "Holdout")
ctrl = makeTuneControlIrace(maxExperiments = 30L, nbIterations = 1L)
ps = makeParamSet(makeNumericParam("shrinkage",
lower = pi * 1e-20,
upper = 5.242e12))
lrn.tune = makeTuneWrapper("classif.gbm",
resampling = rdesc, par.set = ps,
control = ctrl, show.info = FALSE)
res = suppressWarnings(resample(lrn.tune, task = multiclass.task, rdesc))
lrn = makeLearner("classif.rpart")
ctrl = makeTuneControlIrace(
maxExperiments = 30L, nbIterations = 1L,
digits = 5L)
ps = makeParamSet(makeNumericParam("cp", lower = 1e-5, upper = 1e-4))
lrn.tune = makeTuneWrapper(lrn,
resampling = rdesc, par.set = ps,
control = ctrl, show.info = FALSE)
res = resample(lrn.tune, task = multiclass.task, rdesc)
ctrl = makeTuneControlIrace(maxExperiments = 60L, digits = 4)
ps = makeParamSet(makeNumericParam("cp", lower = 1e-5, upper = 1e-4))
lrn.tune = makeTuneWrapper(lrn,
resampling = rdesc, par.set = ps,
control = ctrl, show.info = FALSE)
expect_error(resample(lrn.tune, task = multiclass.task, rdesc))
ctrl = makeTuneControlIrace(maxExperiments = 60L, digits = "a")
ps = makeParamSet(makeNumericParam("cp", lower = 1e-5, upper = 1e-2))
lrn.tune = makeTuneWrapper(lrn,
resampling = rdesc, par.set = ps,
control = ctrl, show.info = FALSE)
expect_error(resample(lrn.tune, task = multiclass.task, rdesc))
ctrl = makeTuneControlIrace(maxExperiments = 60L, digits = c(6L, 7L))
ps = makeParamSet(makeNumericParam("cp", lower = 1e-5, upper = 1e-2))
lrn.tune = makeTuneWrapper(lrn,
resampling = rdesc, par.set = ps,
control = ctrl, show.info = FALSE)
expect_error(resample(lrn.tune, task = multiclass.task, rdesc))
})
test_that("makeTuneControlIrace handles budget parameter", {
rdesc = makeResampleDesc("Holdout", stratify = TRUE, split = 0.1)
ps = makeParamSet(makeIntegerParam("minsplit", lower = 1, upper = 3))
n = 20
expect_error(makeTuneControlIrace(
budget = n, nbIterations = 1L,
minNbSurvival = 1, maxExperiments = n + 2L))
expect_error(makeTuneControlIrace(
budget = n + 2L, nbIterations = 1L,
minNbSurvival = 1, maxExperiments = n))
# check whether it is ok to provide both arguments as long as they are the same
ctrl = makeTuneControlIrace(
budget = n, nbIterations = 1L, minNbSurvival = 1,
maxExperiments = n)
tr1 = tuneParams(makeLearner("classif.rpart"), multiclass.task, rdesc,
par.set = ps, control = ctrl)
expect_true(getOptPathLength(tr1$opt.path) <= n)
})
test_that("Error in hyperparameter tuning with scientific notation for lower/upper boundaries #279", {
ps = makeParamSet(makeNumericParam("shrinkage", lower = 4e-5, upper = 1e-4))
ctrl = makeTuneControlIrace(maxExperiments = 30L, nbIterations = 1L)
rdesc = makeResampleDesc(method = "Holdout")
lrn.tune = makeTuneWrapper("classif.gbm",
resampling = rdesc, par.set = ps,
control = ctrl)
expect_silent(resample(lrn.tune, task = sonar.task, rdesc))
})
# we had a bug here, see issue #627
test_that("irace works with unnamed discrete values", {
lrn = makeLearner("classif.rpart")
ctrl = makeTuneControlIrace(maxExperiments = 30L, nbIterations = 1L)
ps = makeParamSet(
makeDiscreteParam("minsplit", c(2L, 7L))
)
expect_silent(tuneParams(lrn, multiclass.task, hout,
par.set = ps,
control = ctrl))
})
# there was a bug when the column of an opt-path was NA all the way
test_that("irace handles parameters with unsatisfiable requirement gracefully", {
skip_on_os("windows")
lrn = makeLearner("classif.J48")
ctrl = makeTuneControlIrace(
maxExperiments = 20L, nbIterations = 1L,
minNbSurvival = 1L)
ps = makeParamSet(
makeNumericParam("C", 0.1, 0.3, requires = quote(R != R)),
makeLogicalParam("R")) # C never feasible
res = tuneParams(lrn, pid.task, hout, par.set = ps, control = ctrl)
ps = makeParamSet(
makeNumericParam("C", 0.1, 0.3),
makeLogicalParam("R", requires = quote(C > 1))) # R never feasible
expect_silent(tuneParams(lrn, sonar.task, hout, par.set = ps, control = ctrl))
})
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