# This is an example on how a run can be generated and uploaded.
# Doesn't work yet.
# Which task should be solved?
task.id <- 1
task <- downloadOpenMLTask(id = task.id)
# Which Implementation/Learner should be used?
lrn <- makeLearner("classif.rpart", minsplit = 1)
session.hash <- authenticateUser("dominikkirchhoff", "testpasswort")
# Check if implementation lrn is already registered. If not, upload it automatically.
# This is not yet done. The API call is missing.
implementation.id <- getMLRLearnerImplementationId(
learner = lrn,
register = TRUE,
session.hash = session.hash)
# Make a run.
results <- runTask(task = task, learner = lrn, return.mlr.results = TRUE)
# Save the predictions.
output.file <- tempfile()
save(results$run.pred, file = output.file)
# Generate a description object of the run.
# Maybe we should do this in uploadOpenMLRun and save a line of code?
description <- OpenMLRun(
task.id = task.id,
implementation.id = implementation.id,
parameter.settings = makeRunParameterList(lrn))
# Upload the run.
uploadOpenMLRun(description = description, output.files = output.file, session.hash = session.hash)
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