oml_collection {mlr3oml}

OpenML Collection

Description

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

Because collections on OpenML can be modified (ids can be added), it is not possible to cache this object.

mlr3 Intergration

• Obtain a list of mlr3::Tasks using mlr3::as_tasks.

• Obtain a list of mlr3::Resamplings using mlr3::as_resamplings.

• Obtain a list of mlr3::Learners using mlr3::as_learners (if main_entity_type is "run").

• Obtain a mlr3::BenchmarkResult using mlr3::as_benchmark_result (if main_entity_type is "run").

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.

Methods

Public methods

• OMLCollection$new()

• OMLCollection$print()

• OMLCollection$download()

• OMLCollection$clone()

Inherited methods

• mlr3oml::OMLObject$help()

Method new()

Creates a new instance of this R6 class.

Usage

OMLCollection$new(id, test_server = test_server_default())

Arguments

id

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

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 download()

Downloads the whole object for offline usage.

Usage

OMLCollection$download()

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.

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.

Examples

# For technical reasons, examples cannot be included in this R package.
# Instead, these are some relevant resources:
#
# Large-Scale Benchmarking chapter in the mlr3book:
# https://mlr3book.mlr-org.com/chapters/chapter11/large-scale_benchmarking.html
#
# Package Article:
# https://mlr3oml.mlr-org.com/articles/tutorial.html