Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feat: add n features to data table view of archive #91

Merged
merged 3 commits into from
Dec 15, 2023
Merged

feat: add n features to data table view of archive #91

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
f2c7306
feat: add n features to data table view of archive
be-marc Dec 14, 2023
72dd876
chore: update news
be-marc Dec 14, 2023
e142e5f
test: extract_inner_fselect_archives
be-marc Dec 14, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions NEWS.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# mlr3fselect (development version)

* feat: Add number of features to `as.data.table.ArchiveFSelect()`.
* feat: Features can be always included with the `always_include` column role.
* fix: Add `$phash()` method to `AutoFSelector`.
* fix: Include `FSelector` in hash of `AutoFSelector`.
Expand Down
1 change: 1 addition & 0 deletions R/ArchiveFSelect.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -157,6 +157,7 @@ as.data.table.ArchiveFSelect = function(x, ..., exclude_columns = "uhash", measu

# add feature vector
tab[, "features" := lapply(transpose(.SD), function(col) x$cols_x[col]), .SDcols = x$cols_x]
tab[, "n_features" := map(get("features"), length)]

if (x$benchmark_result$n_resample_results) {
# add extra measures
Expand Down
32 changes: 16 additions & 16 deletions tests/testthat/test_ArchiveFSelect.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,45 +61,45 @@ test_that("ArchiveFSelect as.data.table function works", {

# default
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 16)
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", "warnings", "errors", "features", "resample_result"))
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result"))

# extra measure
tab = as.data.table(instance$archive, measures = msr("classif.acc"))
expect_data_table(tab, nrows = 4, ncols = 17)
expect_data_table(tab, nrows = 4, ncols = 18)
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"))
"classif.acc", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_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_data_table(tab, nrows = 4, ncols = 19)
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"))
"classif.acc", "classif.mcc", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result"))

# exclude column
tab = as.data.table(instance$archive, exclude_columns = "timestamp")
expect_data_table(tab, nrows = 4, ncols = 16)
expect_data_table(tab, nrows = 4, ncols = 17)
expect_named(tab, c("age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce",
"runtime_learners", "batch_nr", "uhash", "warnings", "errors", "features", "resample_result"))
"runtime_learners", "batch_nr", "uhash", "warnings", "errors", "features", "n_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_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", "warnings", "errors", "features", "resample_result"))
"runtime_learners", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result"))

# no exclude
tab = as.data.table(instance$archive, exclude_columns = NULL)
expect_data_table(tab, nrows = 4, ncols = 17)
expect_data_table(tab, nrows = 4, ncols = 18)
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"))
"runtime_learners", "timestamp", "batch_nr", "uhash", "warnings", "errors", "features", "n_features", "resample_result"))

# no unnest
tab = as.data.table(instance$archive, unnest = NULL)
expect_data_table(tab, nrows = 4, ncols = 16)
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", "warnings", "errors", "features", "resample_result"))
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result"))

# without benchmark result
instance = FSelectInstanceSingleCrit$new(
Expand All @@ -113,9 +113,9 @@ test_that("ArchiveFSelect as.data.table function works", {
fselector$optimize(instance)

tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 15)
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"))
"runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features"))

# empty archive
instance = FSelectInstanceSingleCrit$new(
Expand Down
14 changes: 7 additions & 7 deletions tests/testthat/test_extract_inner_fselect_archives.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ test_that("extract_inner_fselect_archives function works with resample and cv",

irr = extract_inner_fselect_archives(rr)
expect_data_table(irr, nrows = 8)
expect_named(irr, c("iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(irr, c("iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
})

test_that("extract_inner_fselect_archives function works with resample and repeated cv", {
Expand All @@ -13,7 +13,7 @@ test_that("extract_inner_fselect_archives function works with resample and repea

irr = extract_inner_fselect_archives(rr)
expect_data_table(irr, nrows = 24)
expect_named(irr, c("iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(irr, c("iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
})

test_that("extract_inner_fselect_archives function works with benchmark and cv", {
Expand All @@ -25,7 +25,7 @@ test_that("extract_inner_fselect_archives function works with benchmark and cv",

ibmr = extract_inner_fselect_archives(bmr)
expect_data_table(ibmr, nrows = 16)
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_equal(unique(ibmr$experiment), c(1, 2))
})

Expand All @@ -38,7 +38,7 @@ test_that("extract_inner_fselect_archives function works with benchmark and repe

ibmr = extract_inner_fselect_archives(bmr)
expect_data_table(ibmr, nrows = 48)
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_equal(unique(ibmr$experiment), c(1, 2))
})

Expand All @@ -51,7 +51,7 @@ test_that("extract_inner_fselect_archives function works with multiple tasks", {

ibmr = extract_inner_fselect_archives(bmr)
expect_data_table(ibmr, nrows = 32)
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "age", "glucose", "insulin", "mass", "pedigree", "pregnant", "pressure", "triceps", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_equal(unique(ibmr$experiment), c(1, 2, 3, 4))
})

Expand Down Expand Up @@ -92,7 +92,7 @@ test_that("extract_inner_fselect_archives function works with mixed store instan
bmr = benchmark(grid, store_models = TRUE)

ibmr = extract_inner_fselect_archives(bmr)
expect_data_table(ibmr, ncols = 17)
expect_data_table(ibmr, ncols = 18)
expect_equal(unique(ibmr$experiment), 2)
})

Expand All @@ -105,6 +105,6 @@ test_that("extract_inner_fselect_archives function works with autofselector and

ibmr = extract_inner_fselect_archives(bmr)
expect_data_table(ibmr, nrows = 8)
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_named(ibmr, c("experiment", "iteration", "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width", "classif.ce", "runtime_learners", "timestamp", "batch_nr", "warnings", "errors", "features", "n_features", "resample_result", "task_id", "learner_id", "resampling_id"))
expect_equal(unique(ibmr$experiment), 1)
})