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test_that("error dump is created in train", {
lrn = makeLearner("classif.qda", predict.type = "prob",
config = list(on.learner.error = "quiet", on.error.dump = TRUE))
mod = train(lrn, iris.task, subset = c(1L, 51L, 101L))
expect_class(getFailureModelDump(mod), "dump.frames")
})
test_that("error dump is created in predict", {
lrn = makeLearner("classif.knn", config = list(on.learner.error = "quiet", on.error.dump = TRUE))
lrn$properties = c(lrn$properties, "missings")
task = makeClassifTask("test", data = Sonar, target = "Class")
task$env$data$V1[1:2] = NA
mod = train(lrn, task)
p = predict(mod, task)
expect_class(getPredictionDump(p), "dump.frames")
})
test_that("error dump is created in resample", {
lrn = makeLearner("classif.__mlrmocklearners__2", alpha = 0, config = list(on.learner.error = "quiet", on.error.dump = TRUE))
r = holdout(lrn, multiclass.task)
expect_equal(length(getRRDump(r)), 1)
expect_class(getRRDump(r)[[1]]$train, "dump.frames")
lrn = makeLearner("classif.knn", config = list(on.learner.error = "quiet", on.error.dump = TRUE))
lrn$properties = c(lrn$properties, "missings")
task = makeClassifTask("test", data = Sonar, target = "Class")
task$env$data$V1 = NA
r = bootstrapB632plus(lrn, task, iters = 2)
expect_equal(length(getRRDump(r)), 2)
expect_class(getRRDump(r)[[1]]$predict.test, "dump.frames")
expect_class(getRRDump(r)[[1]]$predict.train, "dump.frames")
})
test_that("error dump is created during tune", {
mlr.options = getMlrOptions()
ps = makeParamSet(
makeDiscreteParam("alpha", values = c(1, 0))
)
configureMlr(on.learner.error = "quiet", on.error.dump = TRUE)
ctrl = makeTuneControlGrid()
lrn = makeLearner("classif.__mlrmocklearners__2")
nodumplrn = makeLearner("classif.__mlrmocklearners__2", config = list(on.error.dump = FALSE))
z = tuneParams(lrn, multiclass.task, hout, par.set = ps, control = ctrl,
measures = getDefaultMeasure(multiclass.task))
expect_equal(length(getOptPathEl(z$opt.path, 1)$extra$.dump), 1)
expect_equal(getOptPathEl(z$opt.path, 1)$extra$.dump[[1]], list())
expect_class(getOptPathEl(z$opt.path, 2)$extra$.dump[[1]]$train, "dump.frames")
z = tuneParams(nodumplrn, multiclass.task, hout, par.set = ps, control = ctrl,
measures = getDefaultMeasure(multiclass.task))
expect_equal(length(getOptPathEl(z$opt.path, 1)$extra$.dump), 1)
expect_equal(getOptPathEl(z$opt.path, 1)$extra$.dump[[1]], list())
expect_equal(length(getOptPathEl(z$opt.path, 2)$extra$.dump), 1)
expect_equal(getOptPathEl(z$opt.path, 2)$extra$.dump[[1]], list())
lrn = makeLearner("classif.knn")
lrn$properties = c(lrn$properties, "missings")
task = makeClassifTask("test", data = Sonar, target = "Class")
task$env$data$V1 = NA
ctrl = makeTuneControlRandom(maxit = 2)
ps = makeParamSet(
makeIntegerParam("k", lower = 1, upper = 5)
)
z = tuneParams(lrn, task, makeResampleDesc("Bootstrap", predict = "both", iters = 2),
par.set = ps, control = ctrl, measures = getDefaultMeasure(multiclass.task))
expect_equal(length(getOptPathEl(z$opt.path, 1)$extra$.dump), 2)
expect_class(getOptPathEl(z$opt.path, 1)$extra$.dump[[1]]$predict.test, "dump.frames")
expect_class(getOptPathEl(z$opt.path, 1)$extra$.dump[[1]]$predict.train, "dump.frames")
do.call(configureMlr, mlr.options)
})
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