Nothing
test_that("ArchiveFSelect access methods work", {
instance = fselect(
fselector = fs("random_search"),
task = tsk("iris"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measures = msr("classif.ce"),
term_evals = 4)
# learner
map(instance$archive$data$uhash, function(uhash) {
expect_learner(instance$archive$learner(uhash = uhash))
})
map(seq(nrow(instance$archive$data)), function(i) {
expect_learner(instance$archive$learner(i))
})
# learners
map(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$learners(uhash = uhash))
expect_learner(instance$archive$learners(uhash = uhash)[[1]])
})
map(seq(nrow(instance$archive$data)), function(i) {
expect_list(instance$archive$learners(i))
expect_learner(instance$archive$learners(i)[[1]])
})
# predictions
map(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$predictions(uhash = uhash))
expect_prediction(instance$archive$predictions(uhash = uhash)[[1]])
})
map(seq(nrow(instance$archive$data)), function(i) {
expect_list(instance$archive$predictions(i))
expect_prediction(instance$archive$predictions(i)[[1]])
})
# resample result
# Issue https://github.com/mlr-org/mlr3/issues/893
# map(instance$archive$data$uhash, function(uhash) {
# expect_resample_result(instance$archive$resample_result(uhash = uhash))
# })
#
# map(seq(nrow(instance$archive$data)), function(i) {
# expect_resample_result(instance$archive$resample_result(i))
# })
})
test_that("ArchiveFSelect as.data.table function works", {
instance = fselect(
fselector = fs("random_search", batch_size = 4),
task = tsk("pima"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measures = msr("classif.ce"),
term_evals = 4)
# default
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 16)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result"))
# extra measure
tab = as.data.table(instance$archive, measures = msr("classif.acc"))
expect_data_table(tab, nrows = 4, ncols = 17)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"classif.acc", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result"))
# extra measures
tab = as.data.table(instance$archive, measures = msrs(c("classif.acc", "classif.mcc")))
expect_data_table(tab, nrows = 4, ncols = 18)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"classif.acc", "classif.mcc", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result"))
# exclude column
tab = as.data.table(instance$archive, exclude_columns = "timestamp")
expect_data_table(tab, nrows = 4, ncols = 16)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "batch_nr", "uhash", "warnings", "errors", "features", "resample_result"))
# exclude columns
tab = as.data.table(instance$archive, exclude_columns = c("timestamp", "uhash"))
expect_data_table(tab, nrows = 4, ncols = 15)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "batch_nr", "warnings", "errors", "features", "resample_result"))
# no exclude
tab = as.data.table(instance$archive, exclude_columns = NULL)
expect_data_table(tab, nrows = 4, ncols = 17)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "timestamp", "batch_nr", "uhash", "warnings", "errors", "features", "resample_result"))
# no unnest
tab = as.data.table(instance$archive, unnest = NULL)
expect_data_table(tab, nrows = 4, ncols = 16)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result"))
# without benchmark result
instance = FSelectInstanceSingleCrit$new(
task = tsk("pima"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
terminator = trm("evals", n_evals = 4),
store_benchmark_result = FALSE)
fselector = fs("random_search", batch_size = 4)
fselector$optimize(instance)
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 15)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features"))
# empty archive
instance = FSelectInstanceSingleCrit$new(
task = tsk("pima"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
terminator = trm("evals", n_evals = 4))
expect_data_table(as.data.table(instance$archive), nrows = 0, ncols = 0)
# row order
instance = fselect(
fselector = fs("random_search", batch_size = 1),
task = tsk("pima"),
learner = lrn("classif.rpart"),
resampling = rsmp("holdout"),
measures = msr("classif.ce"),
term_evals = 10)
tab = as.data.table(instance$archive)
expect_equal(tab$batch_nr, 1:10)
})
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