Interface to OpenML Flows

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().

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 -> 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$clone()

Inherited methods

• mlr3oml::OMLObject$help()

---

Method new()

Creates a new instance of this R6 class.

Usage

OMLFlow$new(id, cache = cache_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.

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 clone()

The objects of this class are cloneable with this method.

Usage

OMLFlow$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

try({
  library("mlr3")
  # mlr3 flow:
  flow = OMLFlow$new(id = 19103)
  # using sugar
  flow = oflw(id = 19103)
  learner = as_learner(flow, "classif")
  # python flow
  python_flow = OMLFlow$new(19090)
  # conversion to pseudo Learner
  plearner = as_learner(python_flow, "classif")
  }, silent = TRUE)