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test_that("resample", {
rin1 = makeResampleInstance(makeResampleDesc("Bootstrap", iters = 4), task = multiclass.task)
rin2 = makeResampleInstance(makeResampleDesc("CV", iters = 7), task = multiclass.task)
rin3 = makeResampleInstance(makeResampleDesc("Subsample", iters = 2), task = multiclass.task)
lrn = makeLearner("classif.lda")
r1 = resample(lrn, multiclass.task, rin1)
r2 = resample(lrn, multiclass.task, rin2)
r3 = resample(lrn, multiclass.task, rin3)
p1 = r1$pred
p2 = r2$pred
p3 = r3$pred
inds = Reduce(c, rin1$test.inds)
y = getTaskTargets(multiclass.task)[inds]
expect_equal(p1$data$id, inds)
expect_equal(p1$data$truth, y)
inds = Reduce(c, rin2$test.inds)
y = getTaskTargets(multiclass.task)[inds]
expect_equal(p2$data$id, inds)
expect_equal(p2$data$truth, y)
inds = Reduce(c, rin3$test.inds)
y = getTaskTargets(multiclass.task)[inds]
expect_equal(p3$data$id, inds)
expect_equal(p3$data$truth, y)
# test printer
expect_output(print(r1), "Resample Result")
cv.i = makeResampleInstance(makeResampleDesc("CV", iters = 3), binaryclass.task)
lrn1 = makeLearner("classif.lda")
lrn2 = makeLearner("classif.lda", predict.type = "prob")
rf1 = resample(lrn1, binaryclass.task, cv.i)$pred
rf2 = resample(lrn2, binaryclass.task, cv.i)$pred
rf3 = resample(lrn2, binaryclass.task, cv.i)$pred
rf3 = setThreshold(rf3, 0)
rf4 = resample(lrn2, binaryclass.task, cv.i)$pred
rf4 = setThreshold(rf4, 1)
expect_equal(rf1$data$response, rf2$data$response)
td = getTaskDesc(binaryclass.task)
f1 = factor(rep(td$positive, cv.i$size), levels = td$class.levels)
expect_equal(rf3$data$response, f1)
f2 = factor(rep(td$negative, cv.i$size), levels = td$class.levels)
expect_equal(rf4$data$response, f2)
ct = makeClassifTask(data = iris[, c("Species", "Petal.Width")], target = "Species")
fit = resample(lrn1, ct, makeResampleDesc("CV", iters = 2))
expect_error(resample("classif.rpart", multiclass.task, makeResampleDesc("Holdout"),
measures = list()), "length >= 1")
})
test_that("resampling, predicting train set works", {
rdesc = makeResampleDesc("CV", iters = 2, predict = "train")
lrn = makeLearner("classif.rpart")
expect_error(resample(lrn, multiclass.task, rdesc), "not compatible with resampling")
rdesc = makeResampleDesc("CV", iters = 2, predict = "train")
lrn = makeLearner("classif.rpart")
m = setAggregation(mmce, train.mean)
r = resample(lrn, multiclass.task, rdesc, measures = m)
expect_false(is.na(r$aggr["mmce.train.mean"]))
expect_false(anyNA(r$pred$time))
expect_false(is.null(r$pred$predict.type))
expect_equal(getTaskDesc(multiclass.task), r$pred$task.desc)
rdesc = makeResampleDesc("CV", iters = 2, predict = "both")
lrn = makeLearner("classif.rpart")
m1 = setAggregation(mmce, train.mean)
m2 = setAggregation(mmce, test.mean)
r = resample(lrn, multiclass.task, rdesc, measures = list(m1, m2))
expect_false(is.na(r$aggr["mmce.train.mean"]))
expect_false(is.na(r$aggr["mmce.test.mean"]))
expect_false(anyNA(r$pred$time))
expect_false(is.null(r$pred$predict.type))
expect_equal(getTaskDesc(multiclass.task), r$pred$task.desc)
})
test_that("ResampleInstance can bew created from string", {
rin = makeResampleInstance("CV", size = 100)
expect_s3_class(rin$desc, "CVDesc")
expect_equal(rin$size, 100)
expect_equal(rin$desc$iters, 10)
rin = makeResampleInstance("CV", task = iris.task, iters = 17, stratify = TRUE)
expect_s3_class(rin$desc, "CVDesc")
expect_equal(rin$size, 150)
expect_equal(rin$desc$iters, 17)
})
test_that("resample checks constraints", {
expect_error(makeResampleInstance("CV", iters = 20, size = 10), "more folds")
expect_error(makeResampleInstance("RepCV", folds = 20, reps = 2L, size = 10), "more folds")
})
test_that("resample returns errors", {
configureMlr(on.learner.error = "quiet")
lrn = makeLearner("classif.__mlrmocklearners__2", alpha = 1)
z = holdout(lrn, multiclass.task)
expect_true(!is.na(z$aggr))
expect_true(is.data.frame(z$err.msgs))
expect_true(nrow(z$err.msgs) == 1L)
expect_true(all(is.na(z$err.msgs$train)))
expect_true(all(is.na(z$err.msgs$predict)))
lrn = makeLearner("classif.__mlrmocklearners__2", alpha = 0)
m = train(lrn, multiclass.task)
expect_true(isFailureModel(m))
z = crossval(lrn, multiclass.task, iters = 2L)
expect_true(is.na(z$aggr))
expect_true(is.data.frame(z$err.msgs))
expect_true(nrow(z$err.msgs) == 2L)
expect_true(all(!is.na(z$err.msgs$train)))
expect_true(all(is.na(z$err.msgs$predict)))
configureMlr(on.learner.error = "stop")
})
# issue #668
test_that("resample has error messages when prediction fails", {
on.learner.error.saved = getMlrOptions()$on.learner.error
on.learner.warning.saved = getMlrOptions()$on.learner.warning
configureMlr(on.learner.error = "quiet")
configureMlr(on.learner.warning = "quiet")
lrn = makeLearner("classif.knn")
lrn$properties = c(lrn$properties, "missings")
task = makeClassifTask("test", data = Sonar, target = "Class")
task$env$data$V1[1:2] = NA
r = crossval(lrn, task)
expect_false(all(is.na(r$err.msgs$predict)))
configureMlr(on.learner.error = on.learner.error.saved)
configureMlr(on.learner.warning = on.learner.warning.saved)
})
test_that("resample is extended by an additional measure", {
lrn = makeLearner("classif.rpart", predict.type = "prob")
# check if it works with test, both and train
predict = c("train", "test", "both")
# check if it works with different aggregation methods
aggr = list(test.mean, test.median, test.sd, test.range, test.join)
for (a in aggr) {
for (p in predict) {
rdesc = makeResampleDesc("CV", iter = 3, predict = p)
measures = list(mmce, ber, auc, brier)
# set aggregation method
measures = lapply(measures, setAggregation, a)
if (p == "train") measures = lapply(measures, setAggregation, train.mean)
# create ResampleResult with all measures
res.all = resample(lrn, binaryclass.task, rdesc, measures)
# create ResampleResult with one measure and add the other ones afterwards
res = resample(lrn, binaryclass.task, rdesc, measures[[1]])
res.add = addRRMeasure(res, measures[-1])
# check if both ResampleResult objects are equal
expect_equal(res.all$measures.train, res.add$measures.train)
expect_equal(res.all$measures.test, res.add$measures.test)
expect_equal(res.all$aggr, res.add$aggr)
}
}
# keep.pred must be TRUE
res = resample(lrn, binaryclass.task, cv3, mmce, keep.pred = FALSE)
expect_error(addRRMeasure(res, auc), "keep.pred")
})
test_that("resample printer respects show.info", {
show.info.saved = getMlrOptions()$show.info
lrn = makeLearner("regr.lm")
configureMlr(show.info = TRUE)
suppressMessages(expect_message(resample(lrn, bh.task, cv10, list(mape, medae, mse))))
configureMlr(show.info = FALSE)
expect_silent(resample(lrn, bh.task, cv10, list(mape, medae, mse)))
configureMlr(show.info = show.info.saved)
})
test_that("resample drops unseen factors in predict data set", {
data = data.frame(
a = c("a", "b", "a", "b", "a", "c"),
b = c(1, 1, 2, 2, 2, 1),
trg = c("a", "b", "a", "b", "a", "b"),
stringsAsFactors = TRUE)
task = makeClassifTask("unseen.factors", data, "trg")
resinst = makeResampleInstance("Holdout", task)
resinst$train.inds[[1]] = 1:4
resinst$test.inds[[1]] = 5:6
lrn = makeLearner("classif.logreg", fix.factors.prediction = FALSE)
model = train(lrn, subsetTask(task, 1:4, features = getTaskFeatureNames(task)))
expect_error(predict(model, subsetTask(task, 5:6)), "factor a has new levels c")
expect_error(resample(lrn, task, resinst), "factor a has new levels c")
lrn = makeLearner("classif.logreg", fix.factors.prediction = TRUE)
model = train(lrn, subsetTask(task, 1:4))
expect_warning(predict(model, subsetTask(task, 5:6)), "produced NAs because of new factor levels")
expect_warning(resample(lrn, task, resinst), "produced NAs because of new factor levels")
# do it manually
train_task = makeClassifTask("unseen.factors", data[1:4, ], "trg", fixup = "quiet") # quiet becasue
# we get dropped factors warning (which we want here)
model = train(lrn, train_task)
expect_warning(predict(model, newdata = data[5:6, ]), "produced NAs because of new factor levels")
})
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