oml_flow {mlr3oml}

Interface to OpenML Flows

Description

This is the class for flows served on OpenML. Flows represent machine learning algorithms. This object can also be constructed using the sugar function oflw().

mlr3 Integration

• Obtain a mlr3::Learner using mlr3::as_learner().

Super class

mlr3oml::OMLObject -> OMLFlow

Active bindings

parameter

(data.table)
The parameters of the flow.

dependencies

(character())
The dependencies of the flow.

tags

(character())
Returns all tags of the object.

Methods

Public methods

• OMLFlow$new()

• OMLFlow$print()

• OMLFlow$download()

• OMLFlow$clone()

Inherited methods

• mlr3oml::OMLObject$help()

Method new()

Creates a new instance of this R6 class.

Usage

OMLFlow$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

OMLFlow$print()

Method download()

Downloads the whole object for offline usage.

Usage

OMLFlow$download()

Method clone()

The objects of this class are cloneable with this method.

Usage

OMLFlow$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