Nothing
context("PipeOpOVRSplit")
test_that("PipeOpOVRSplit - basic properties", {
po = PipeOpOVRSplit$new()
expect_pipeop(po)
expect_data_table(po$input, nrows = 1)
expect_data_table(po$output, nrows = 1)
expect_pipeop_class(PipeOpOVRSplit)
})
test_that("PipeOpOVRSplit - train and predict", {
# toy task to split
dat = data.table(target = as.factor(rep(c("a", "b", "rest"), each = 10)), feature = rnorm(30))
tsk = TaskClassif$new("test", backend = dat, target = "target")
po = PipeOpOVRSplit$new()
tout = train_pipeop(po, list(tsk))
expect_equal(po$state$levels, tsk$class_names)
expect_multiplicity(tout[[1]])
expect_list(tout[[1]], len = 3)
expect_named(tout[[1]], tsk$class_names)
expect_true(all(pmap_lgl(list(tout[[1]], names(tout[[1]])), .f = function(task, name) {
expect_task(task)
all(task$target_names == tsk$target_names) && task$positive == name && task$negative == "rest." &&
all.equal(task$truth(), factor(ifelse(tsk$truth() == task$positive, task$positive, "rest."), levels = c(task$positive, "rest.")))
})))
pout = predict_pipeop(po, list(tsk))
expect_multiplicity(pout[[1]])
expect_list(pout[[1]], len = 3)
expect_named(pout[[1]], tsk$class_names)
expect_true(all(pmap_lgl(list(pout[[1]], names(pout[[1]])), .f = function(task, name) {
expect_task(task)
task$target_names == tsk$target_names && task$positive == name && task$negative == "rest." &&
all.equal(task$truth(), factor(ifelse(tsk$truth() == task$positive, task$positive, "rest."), levels = c(task$positive, "rest.")))
})))
})
context("PipeOpOVRUnite")
test_that("PipeOpOVRUnite - basic properties", {
po = PipeOpOVRUnite$new()
expect_pipeop(po)
expect_data_table(po$input, nrows = 1)
expect_data_table(po$output, nrows = 1)
expect_pipeop_class(PipeOpOVRUnite)
})
test_that("PipeOpOVRUnite- train and predict", {
skip_if_not_installed("rpart")
# toy tasks that are splitted, trained and predicted manually
feature = rep(c(1, 0), c(10, 20))
dat1 = data.table(target = as.factor(rep(c("a", "rest"), c(10, 20))), feature = feature)
dat2 = data.table(target = as.factor(rep(c("rest", "b", "rest"), c(10, 10, 10))), feature = feature)
dat3 = data.table(target = as.factor(rep(c("rest", "c"), c(20, 10))), feature = feature)
tsk1 = TaskClassif$new("t1", backend = dat1, target = "target", positive = "a")
tsk2 = TaskClassif$new("t2", backend = dat2, target = "target", positive = "b")
tsk3 = TaskClassif$new("t3", backend = dat3, target = "target", positive = "c")
po = PipeOpOVRUnite$new()
lrn = LearnerClassifRpart$new()
# predict_type "prob"
lrn$predict_type = "prob"
tin = map(list(tsk1, tsk2, tsk3), .f = function(task) {
lrn$train(task)
lrn$predict(task)
})
pout = po$predict(list(as.Multiplicity(tin)))
expect_prediction_classif(pout[[1]])
# predict_type "response"
lrn$predict_type = "response"
tin = map(list(tsk1, tsk2, tsk3), .f = function(task) {
lrn$train(task)
lrn$predict(task)
})
pout = po$predict(list(as.Multiplicity(tin)))
expect_prediction_classif(pout[[1]])
# NA handling
na_response = tin[[1]]$response
na_response[1] = NA
tin[[1]] = PredictionClassif$new(row_ids = tin[[1]]$row_ids, truth = tin[[1]]$truth, response = na_response)
pout = po$predict(list(as.Multiplicity(tin)))
expect_prediction_classif(pout[[1]])
expect_equal(pout[[1]]$prob[1, ], c(a = 1/3, b = 1/3, c = 1/3))
# error handling
tin[[1]] = PredictionClassif$new(row_ids = tin[[1]]$row_ids, truth = tin[[1]]$truth)
expect_error(po$predict(list(as.Multiplicity(tin))), regexp = "PipeOpOVRUnite input predictions had missing 'prob' and missing 'response' values")
})
context("PipeOpOVRSplit and PipeOpOVRUnite")
test_that("PipeOpOVRSplit and PipeOpOVRUnite - train and predict", {
skip_if_not_installed("rpart")
# same toy task but now we compare the results to the automated Graph's results
feature = rep(c(1, 0), c(10, 20))
dat0 = data.table(target = as.factor(rep(c("a", "b", "c"), each = 10)), feature = feature)
dat1 = data.table(target = as.factor(rep(c("a", "rest"), c(10, 20))), feature = feature)
dat2 = data.table(target = as.factor(rep(c("rest", "b", "rest"), c(10, 10, 10))), feature = feature)
dat3 = data.table(target = as.factor(rep(c("rest", "c"), c(20, 10))), feature = feature)
tsk0 = TaskClassif$new("t0", backend = dat0, target = "target")
tsk1 = TaskClassif$new("t1", backend = dat1, target = "target", positive = "a")
tsk2 = TaskClassif$new("t2", backend = dat2, target = "target", positive = "b")
tsk3 = TaskClassif$new("t3", backend = dat3, target = "target", positive = "c")
po = PipeOpOVRUnite$new()
lrn = LearnerClassifRpart$new()
tin = map(list(tsk1, tsk2, tsk3), .f = function(task) {
lrn$train(task)
lrn$predict(task)
})
pout_ref = po$predict(list(as.Multiplicity(tin)))
gr = PipeOpOVRSplit$new() %>>% LearnerClassifRpart$new() %>>% PipeOpOVRUnite$new()
expect_graph(gr)
tout = gr$train(tsk0)
expect_list(gr$state$ovrunite, len = 0)
expect_null(tout[[1]])
pout = gr$predict(tsk0)
expect_equal(pout_ref[[1]]$prob, pout[[1]]$prob)
# setting weights to zero results in uniform probs
gr$param_set$values$ovrunite.weights = rep(0, 3)
expect_true(all.equal(unique(gr$predict(tsk0)[[1]]$prob), t(c(a = 1/3, b = 1/3, c = 1/3))))
})
test_that("PipeOpOVRSplit and PipeOpOVRUnite - task size", {
skip_if_not_installed("rpart")
gr = PipeOpOVRSplit$new() %>>% LearnerClassifRpart$new() %>>% PipeOpOVRUnite$new()
gr$train(tsk("iris")$filter(c(1:30, 51:80, 101:130)))
prd = gr$predict(tsk("iris")$filter(c(1:30, 51:80, 101:130)))[[1]]
expect_prediction_classif(prd)
expect_true(nrow(prd$data$prob) == 90)
})
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