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mlr3oml (version 0.9.0)

oml_collection: 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.

Arguments

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

Inherited methods

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. tools:::Rd_expr_doi("10.1145/2641190.2641198").

Examples

Run this code
# 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

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