mlr3oml allows to create mlr3 tasks
directly from OpenML data sets. Furthermore, you
can also obtain the data and the resampling for a given OpenML task.
Caching can be enabled by setting the option "mlr3oml.cache".
Uploading to OpenML is currently not supported, use the OpenML
package package for this.
library("mlr3")
library("mlr3oml")
# be less verbose
lgr::get_logger("mlr3oml")$set_threshold("warn")
# retrieve data set as task from OML
tsk("oml", data_id = 31)## <TaskClassif:credit-g> (1000 x 21)
## * Target: class
## * Properties: twoclass
## * Features (20):
## - fct (13): checking_status, credit_history, employment,
## foreign_worker, housing, job, other_parties, other_payment_plans,
## own_telephone, personal_status, property_magnitude, purpose,
## savings_status
## - int (7): age, credit_amount, duration, existing_credits,
## installment_commitment, num_dependents, residence_since
# retrieve a regular task from OML
tsk("oml", task_id = 59)## <TaskClassif:Task 59: iris (Supervised Classification)> (150 x 5)
## * Target: class
## * Properties: multiclass
## * Features (4):
## - dbl (4): petallength, petalwidth, sepallength, sepalwidth
# retrieve resampling from OML
rsmp("oml", task_id = 59)## <ResamplingCustom> with 10 iterations
## * Instantiated: TRUE
## * Parameters: list()
# R6 class for data sets
oml_data = OMLData$new(61)
oml_data$name## [1] "iris"
oml_data$nrow## [1] 150
oml_data$ncol## [1] 5
oml_data$data## sepallength sepalwidth petallength petalwidth class
## 1: 5.1 3.5 1.4 0.2 Iris-setosa
## 2: 4.9 3.0 1.4 0.2 Iris-setosa
## 3: 4.7 3.2 1.3 0.2 Iris-setosa
## 4: 4.6 3.1 1.5 0.2 Iris-setosa
## 5: 5.0 3.6 1.4 0.2 Iris-setosa
## ---
## 146: 6.7 3.0 5.2 2.3 Iris-virginica
## 147: 6.3 2.5 5.0 1.9 Iris-virginica
## 148: 6.5 3.0 5.2 2.0 Iris-virginica
## 149: 6.2 3.4 5.4 2.3 Iris-virginica
## 150: 5.9 3.0 5.1 1.8 Iris-virginica
# R6 class for tasks
oml_task = OMLTask$new(31)
oml_task$name## [1] "Task 31: credit-g (Supervised Classification)"
oml_task$nrow## [1] 1000
oml_task$ncol## [1] 21
oml_task$task## <TaskClassif:Task 31: credit-g (Supervised Classification)> (1000 x 21)
## * Target: class
## * Properties: twoclass
## * Features (20):
## - fct (13): checking_status, credit_history, employment,
## foreign_worker, housing, job, other_parties, other_payment_plans,
## own_telephone, personal_status, property_magnitude, purpose,
## savings_status
## - int (7): age, credit_amount, duration, existing_credits,
## installment_commitment, num_dependents, residence_since
oml_task$resampling## <ResamplingCustom> with 10 iterations
## * Instantiated: TRUE
## * Parameters: list()
# list oml data sets with 5 features and 50 - 200 instances
tab = list_oml_data_sets(number_features = 5, number_instances = c(50, 200))
head(tab[, .(data_id, name)])## data_id name
## 1: 61 iris
## 2: 199 fruitfly
## 3: 214 baskball
## 4: 329 hayes-roth
## 5: 346 aids
## 6: 668 witmer_census_1980
# list first 10 oml tasks
tab = list_oml_tasks(limit = 10)
tab[, .(task_id, data_id, name)]## task_id data_id name
## 1: 2 2 anneal
## 2: 3 3 kr-vs-kp
## 3: 4 4 labor
## 4: 5 5 arrhythmia
## 5: 6 6 letter
## 6: 7 7 audiology
## 7: 8 8 liver-disorders
## 8: 9 9 autos
## 9: 10 10 lymph
## 10: 11 11 balance-scale