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OpenML Collection

Source: R/OMLCollection.R
oml_collection.Rd

This is the class for collections (previously known as studies) served on https://www.openml.org. A collection can either be a task collection or run collection. This object can also be constructed using the sugar function ocl().

Run Collection

A run collection contains runs, flows, datasets and tasks. The primary object are the runs (main_entity_type is "run"). The the flows, datasets and tasks are those used in the runs.

Task Collection A task collection (main_entity_type = "task") contains tasks and datasets. The primary object are the tasks (main_entity_type is "task"). The datasets are those used in the tasks.

Note: All Benchmark Suites on OpenML are also collections.

Caching

The OpenML collection itself cannot be not cached, this is because it can be modified in-place on the server, e.g. by adding or removing tasks or runs. The construction argument cache therefore only controls wether caching is applied to the OpenML objects that are contained in the collection.

mlr3 Intergration

References

Vanschoren J, van Rijn JN, Bischl B, Torgo L (2014). “OpenML.” ACM SIGKDD Explorations Newsletter, 15(2), 49--60. doi:10.1145/2641190.2641198 .

Super class

mlr3oml::OMLObject -> OMLCollection

Active bindings

desc

(list())
Colllection description (meta information), downloaded and converted from the JSON API response.

parquet

(logical(1))
Whether to use parquet.

main_entity_type

(character(n))
The main entity type, either "run" or "task".

flow_ids

(integer(n))
An vector containing the flow ids of the collection.

data_ids

(integer(n))
An vector containing the data ids of the collection.

run_ids

(integer(n))
An vector containing the run ids of the collection.

task_ids

(integer(n))
An vector containing the task ids of the collection.

runs

(data.table()) A data.table summarizing the runs included in the collection. Returns NULL for Task Collections.

flows

(data.table()) A data.table summarizing the flows included in the collection. Returns NULL for Task Collections.

data

(data.table()) A data.table summarizing the datasets included in the collection.

tasks

(data.table()) A data.table summarizing the tasks included in the collection.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OMLCollection$new(
  id,
  cache = cache_default(),
  parquet = parquet_default(),
  test_server = test_server_default()
)

Arguments

id

(integer(1))
OpenML id for the object.

cache

(logical(1) | character(1))
See field cache for an explanation of possible values. Defaults to value of option "mlr3oml.cache", or FALSE if not set. The collection itself is not cached, this is because it can be modified in-place on OpenML, e.g. by adding or removing tasks or runs. This parameter therefore only controls whether the contained elements are cached when loaded, e.g. when accessing the included tasks.

parquet

(logical(1))
Whether to use parquet instead of arff. If parquet is not available, it will fall back to arff. Defaults to value of option "mlr3oml.parquet" or FALSE if not set.

test_server

(character(1))
Whether to use the OpenML test server or public server. Defaults to value of option "mlr3oml.test_server", or FALSE if not set.

Method print()

Prints the object.

Usage

OMLCollection$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

OMLCollection$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

try({
  library("mlr3")
  # OpenML Run collection:
  run_collection = OMLCollection$new(id = 232)
  # using sugar
  run_collection = ocl(id = 232)
  print(run_collection)

  # OpenML task collection:
  task_collection = OMLCollection$new(id = 258)
  # using sugar
  task_collection = ocl(id = 258)
  print(task_collection)
  }, silent = TRUE)
#> <OMLCollection: 232>
#>  * data:  2
#>  * tasks: 2
#>  * flows: 2
#>  * runs:  4
#> <OMLCollection: 258>
#>  * data:  12
#>  * tasks: 12