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
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
OMLFlow$new(id, cache = cache_default(), test_server = test_server_default())
Arguments
id
(
integer(1)
)
OpenML id for the object.cache
(
logical(1)
|character(1)
)
See fieldcache
for an explanation of possible values. Defaults to value of option"mlr3oml.cache"
, orFALSE
if not set.test_server
(
character(1)
)
Whether to use the OpenML test server or public server. Defaults to value of option"mlr3oml.test_server"
, orFALSE
if not set.
Examples
try({
library("mlr3")
# mlr3 flow:
flow = OMLFlow$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)