context("convertOMLRunToBMR")
test_that("convertOMLRunToBMR", {
checkBMR = function(bmr) {
expect_is(bmr, "BenchmarkResult")
res.classes = c("character", "character", "TaskDesc", "data.frame", "data.frame", "numeric",
"Prediction", "list", "data.frame", "list", "numeric", "Learner" )
for (j in seq_along(res.classes)) expect_is(bmr$results[[1]][[1]][[j]], res.classes[j])
expect_equal(dim(bmr$results[[1]][[1]]$measures.train), dim(bmr$results[[1]][[1]]$measures.test))
for (j in seq_along(bmr$measures)) {
expect_is(bmr$measures[[j]], "Measure")
expect_equal(getBMRMeasures(bmr)[[j]], bmr$measures[[j]])
expect_equal(getBMRMeasureIds(bmr)[[j]], bmr$measures[[j]]$id)
}
for (j in seq_along(bmr$learners)) expect_is(bmr$learners[[j]], "Learner")
# check getBMRPredictions
preds = getBMRPredictions(bmr, as.df = FALSE)
expect_true(is.list(preds))
preds1 = preds[[1L]]
expect_true(is.list(preds1))
preds11 = preds1[[1L]]
expect_is(preds11, "Prediction")
p = getBMRPerformances(bmr, as.df = TRUE)
expect_is(p, "data.frame")
expect_true(nrow(p) > 1)
a = getBMRAggrPerformances(bmr, as.df = TRUE)
expect_is(a, "data.frame")
expect_true(nrow(a) == 1)
}
with_empty_cache({
with_main_server({
class.tasks = lapply(c(3064, 3503), getOMLTask)
regr.tasks = lapply(c(2284, 5046), getOMLTask)
## Supervised Classification with predict.type = "response"
#run.class = lapply(class.tasks, function(x) runTaskMlr(x, makeLearner("classif.rpart", predict.type = "response")))
#run.class.ids = sapply(run.class, function(x) uploadOMLRun(x, tags = "convertBMR"))
run.class.ids = c(1854986, 1854987)
## Supervised Classification tasks with different estimation procedures
#run.class.prob = lapply(class.tasks, function(x) runTaskMlr(x, makeLearner("classif.rpart", predict.type = "prob")))
#run.class.prob.ids = sapply(run.class.prob, function(x) uploadOMLRun(x, tags = "convertBMR"))
run.class.prob.ids = c(1854989, 1854990)
## Supervised Regression
#run.regr = lapply(regr.tasks, function(x) runTaskMlr(x, makeLearner("regr.rpart")))
#run.regr.ids = sapply(run.regr, function(x) uploadOMLRun(x, tags = "convertBMR"))
run.regr.ids = c(1854996, 1854998)
### Supervised Classification tasks with different estimation procedures
run.class.list = lapply(run.class.ids, getOMLRun)
bmr = lapply(run.class.list, convertOMLRunToBMR, measures = c("area_under_roc_curve"))
for(i in 1:length(bmr)) {
checkBMR(bmr[[i]])
expect_equal(bmr[[i]]$measures[[1]]$id, "auc")
expect_numeric(getPredictionProbabilities(getBMRPredictions(bmr[[i]])[[1]][[1]]))
}
expect_is(mlr::mergeBenchmarkResults(bmr), "BenchmarkResult")
### Supervised Classification with predict.type = "response"
run.class.prob.list = lapply(run.class.prob.ids, getOMLRun)
bmr = lapply(run.class.prob.list, convertOMLRunToBMR, measures = c("area_under_roc_curve"))
for(i in 1:length(bmr)) {
checkBMR(bmr[[i]])
expect_error(getPredictionProbabilities(getBMRPredictions(bmr[[i]])[[1]][[1]]), "Probabilities not present")
}
expect_is(mlr::mergeBenchmarkResults(bmr), "BenchmarkResult")
### Supervised Regression
run.regr.list = lapply(run.regr.ids, getOMLRun)
bmr = lapply(run.regr.list, convertOMLRunToBMR, measures = c("root_mean_squared_error"))
for(i in 1:length(bmr)) {
checkBMR(bmr[[i]])
}
expect_is(mlr::mergeBenchmarkResults(bmr), "BenchmarkResult")
})
})
})
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