Nothing
test_that("results are ordered", {
grid = data.table(
task = tsks(c("iris", "sonar")),
learner = lrns(c("classif.featureless", "classif.debug")),
resampling = rsmps("cv", folds = 3)
)
grid$resampling = pmap(grid, function(task, resampling, ...) resampling$clone(deep = TRUE)$instantiate(task))
bmr = benchmark(grid, store_models = TRUE)
rdata = get_private(bmr)$.data
tab = rdata$as_data_table()
expect_equal(rdata$uhashes(), rdata$data$uhashes$uhash)
expect_equal(unique(hashes(tab$task)), hashes(grid$task))
expect_equal(unique(hashes(tab$learner)), hashes(grid$learner))
expect_equal(unique(hashes(tab$resampling)), hashes(grid$resampling))
rdata$data$uhashes$uhash = rev(rdata$data$uhashes$uhash)
tab = rdata$as_data_table()
expect_equal(rdata$uhashes(), rdata$data$uhashes$uhash)
expect_equal(unique(hashes(tab$task)), rev(hashes(grid$task)))
expect_equal(unique(hashes(tab$learner)), rev(hashes(grid$learner)))
expect_equal(unique(hashes(tab$resampling)), rev(hashes(grid$resampling)))
rr = resample(tsk("pima"), lrn("classif.rpart"), rsmp("holdout"))
rdata$combine(get_private(rr)$.data)
expect_resultdata(rdata)
expect_equal(rdata$uhashes()[3], rr$uhash)
# remove rr in the middle
uhashes = rdata$uhashes()
rdata$data$fact = rdata$data$fact[!list(uhashes[2])]
rdata$sweep()
expect_resultdata(rdata, TRUE)
expect_equal(rdata$uhashes(), uhashes[c(1, 3)])
# test discard
expect_true(!every(map(rdata$data$fact$learner_state, "model"), is.null))
expect_true(!some(map(rdata$data$tasks$task, "backend"), is.null))
rdata$discard(models = TRUE)
expect_true(every(map(rdata$data$fact$learner_state, "model"), is.null))
rdata$discard(backends = TRUE)
expect_true(every(map(rdata$data$tasks$task, "backend"), is.null))
})
test_that("mlr3tuning use case", {
task = tsk("iris")
learners = lrns(c("classif.rpart", "classif.rpart", "classif.rpart"))
learners[[1]]$param_set$values = list(xval = 0, cp = 0.1)
learners[[2]]$param_set$values = list(xval = 0, cp = 0.2)
learners[[3]]$param_set$values = list(xval = 0, cp = 0.3)
resampling = rsmp("holdout")
bmr = benchmark(benchmark_grid(task, learners, resampling))
rdata = get_private(bmr)$.data
expect_resultdata(rdata)
expect_data_table(rdata$data$fact, nrows = 3L)
expect_data_table(rdata$data$tasks, nrows = 1L)
expect_data_table(rdata$data$learners, nrows = 1L)
expect_data_table(rdata$data$learner_components, nrows = 3L)
expect_data_table(rdata$data$resamplings, nrows = 1L)
expect_set_equal(map_dbl(bmr$learners$learner, function(l) l$param_set$values$cp), 1:3 / 10)
get_params = function(l) l$param_set$values$cp
has_state = function(l) length(l$state) > 0L
expect_set_equal(map_dbl(bmr$learners$learner, get_params), 1:3 / 10)
expect_true(all(!map_lgl(bmr$learners$learner, has_state)))
aggr = bmr$aggregate()
expect_set_equal(map_dbl(map(aggr$resample_result, "learner"), get_params), 1:3 / 10)
expect_true(all(!map_lgl(map(aggr$resample_result, "learner"), has_state)))
scores = bmr$score()
expect_set_equal(map_dbl(scores$learner, get_params), 1:3 / 10)
expect_true(every(scores$learner, has_state))
learner_states = rdata$learner_states()
expect_list(learner_states, any.missing = FALSE, len = 3)
expect_set_equal(map_dbl(learner_states, function(l) l$param_vals$cp), 1:3 / 10)
})
test_that("predict set selection", {
task = tsk("mtcars")
learner = lrn("regr.rpart", predict_sets = c("train", "test"))
resampling = rsmp("holdout")
rr = resample(task, learner, resampling)
p1 = rr$predictions("train")[[1]]
p2 = rr$predictions("test")[[1]]
expect_prediction(p1)
expect_prediction(p2)
expect_disjunct(p1$row_ids, p2$row_ids)
p1 = rr$prediction("train")
p2 = rr$prediction("test")
expect_prediction(p1)
expect_prediction(p2)
expect_disjunct(p1$row_ids, p2$row_ids)
})
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