Nothing
test_that("Construction", {
task = tsk("iris")
p = PredictionClassif$new(row_ids = task$row_ids, truth = task$truth(), response = task$truth())
expect_prediction(p)
expect_prediction_classif(p)
})
test_that("Internally constructed Prediction", {
task = tsk("iris")
lrn = lrn("classif.featureless")
lrn$predict_type = "prob"
p = lrn$train(task)$predict(task)
expect_prediction(p)
expect_prediction_classif(p, task = task)
p = PredictionClassif$new(row_ids = task$row_ids, truth = task$truth(), prob = p$prob, check = TRUE)
expect_set_equal(p$predict_types, c("response", "prob"))
})
test_that("setting threshold binaryclass", {
task = tsk("sonar")
lrn = lrn("classif.rpart", predict_type = "prob")
p = lrn$train(task)$predict(task)
expect_factor(p$response, levels = task$class_names)
expect_equal(as.character(p$response), colnames(p$prob)[max.col(p$prob)])
response_before = p$response
expect_false(withVisible(p$set_threshold(0.5))$visible) # 356
expect_factor(p$response, levels = task$class_names, any.missing = FALSE)
expect_equal(p$response, response_before)
expect_lt(p$score(msr("classif.ce")), 0.25)
p$set_threshold(0)
expect_factor(p$response, levels = task$class_names, any.missing = FALSE)
expect_true(all(as.character(p$response) == task$positive | p$prob[, task$positive] == 0))
expect_gt(p$score(msr("classif.ce")), 0.25)
p$set_threshold(1)
expect_factor(p$response, levels = task$class_names, any.missing = FALSE)
expect_true(all(as.character(p$response) == task$negative | p$prob[, task$negative] == 0))
expect_gt(p$score(msr("classif.ce")), 0.25)
})
test_that("setting threshold multiclass", {
task = tsk("zoo")
lrn = lrn("classif.rpart", predict_type = "prob")
p = lrn$train(task)$predict(task)
# a small fix for our tests ... Add a small number to all probabilities so that
# we can knock off single labels
p$data$prob = t(apply(p$data$prob, 1, function(x) {
x = x + 0.01
x / sum(x)
}))
expect_factor(p$response, levels = task$class_names)
expect_equal(as.character(p$response), colnames(p$prob)[max.col(p$prob)])
prob_before = p$prob
response_before = p$response
expect_error({
p$set_threshold(c(0.5, 0.5))
}, "length") # check for correct length(threshold) = nclass
x = p$set_threshold(set_names(c(1, 1, 1, 1, 1, 1, 1), task$class_names))
expect_factor(x$response, levels = task$class_names, any.missing = FALSE)
expect_equal(x$response, response_before)
threshold = set_names(c(0, 1, 1, 1, 1, 1, 1), task$class_names)
x = p$set_threshold(threshold)
expect_factor(x$response, levels = task$class_names, any.missing = FALSE)
expect_equal(as.character(unique(x$response)), task$class_names[1L])
})
test_that("setting threshold edge cases (#452)", {
learner = lrn("classif.rpart", predict_type = "prob")
t = tsk("iris")
prd = learner$train(t)$predict(t$clone()$filter(c(1, 51, 101)))
prd$set_threshold(c(setosa = 0, versicolor = 0, virginica = 0), ties_method = "first")
expect_equal(as.character(prd$response), c("setosa", "versicolor", "versicolor"))
prd$set_threshold(c(setosa = 0, versicolor = 0, virginica = 0), ties_method = "last")
expect_equal(as.character(prd$response), c("setosa", "virginica", "virginica"))
prd$set_threshold(c(setosa = 1, versicolor = 0, virginica = 0), ties_method = "first")
expect_equal(as.character(prd$response), c("setosa", "versicolor", "versicolor"))
prd$set_threshold(c(setosa = 1, versicolor = 0, virginica = 0), ties_method = "last")
expect_equal(as.character(prd$response), c("setosa", "virginica", "virginica"))
prd$set_threshold(c(setosa = 0, versicolor = 1, virginica = 0), ties_method = "first")
expect_equal(as.character(prd$response), c("setosa", "virginica", "virginica"))
prd$set_threshold(c(setosa = 0, versicolor = 1, virginica = 0), ties_method = "last")
expect_equal(as.character(prd$response), c("setosa", "virginica", "virginica"))
prd$set_threshold(c(setosa = 0, versicolor = 0, virginica = 1), ties_method = "first")
expect_equal(as.character(prd$response), c("setosa", "versicolor", "versicolor"))
prd$set_threshold(c(setosa = 0, versicolor = 0, virginica = 1), ties_method = "last")
expect_equal(as.character(prd$response), c("setosa", "versicolor", "versicolor"))
})
test_that("confusion", {
task = tsk("iris")
lrn = lrn("classif.featureless")
lrn$predict_type = "prob"
p = lrn$train(task)$predict(task)
cm = p$confusion
expect_matrix(cm, nrows = 3, ncols = 3, any.missing = FALSE)
expect_equal(colnames(p$confusion), task$class_names)
expect_equal(rownames(p$confusion), task$class_names)
expect_equal(names(dimnames(cm)), c("response", "truth"))
})
test_that("c", {
task = tsk("iris")
lrn = lrn("classif.featureless")
lrn$predict_type = "prob"
rr = resample(task, lrn, rsmp("cv", folds = 3))
pred = do.call(c, rr$predictions())
expect_prediction_classif(pred)
dt = as.data.table(pred)
expect_data_table(dt, nrows = task$nrow, ncols = 6L, any.missing = FALSE)
conf = pred$confusion
expect_equal(sum(conf), 150L)
expect_equal(rownames(conf), task$class_names)
expect_equal(colnames(conf), task$class_names)
expect_equal(conf, Reduce("+", map(rr$predictions(), "confusion")))
# duplicates are detected?
p1 = get_private(rr)$.data$data$fact$prediction[[1]]$test
p2 = get_private(rr)$.data$data$fact$prediction[[1]]$test
p3 = c(p1, p2, keep_duplicates = FALSE)
expect_equal(sort(p1$data$row_ids), sort(p2$data$row_ids))
expect_equal(sort(p1$data$row_ids), sort(p3$data$row_ids))
expect_factor(p3$response, len = length(p1$response), any.missing = FALSE)
expect_matrix(p3$prob, nrows = nrow(p1$prob), ncols = ncol(p1$prob))
})
test_that("as_prediction_classif", {
task = tsk("penguins")
learner = lrn("classif.featureless", method = "weighted.sample")
p = learner$train(task)$predict(task)
tab = as.data.table(p)
p2 = as_prediction_classif(tab)
expect_equal(tab, as.data.table(p2))
# issue #870
tab = data.frame(row_ids = 1:3, truth = factor(c("a", "b", "a")), response = factor(c("a", "b", "b")))
expect_class(as_prediction_classif(tab), "PredictionClassif")
})
test_that("#615", {
task = tsk("iris")
training <- task$clone()$filter(1:100)
testing <- task$clone()$filter(101:150)
l = lrn("classif.rpart")
l$train(training)
expect_equal(unname(l$predict(testing)$score()), 1)
})
test_that("filtering", {
task = tsk("iris")
p = PredictionClassif$new(row_ids = task$row_ids, truth = task$truth(), response = task$truth())
p2 = p$clone()$filter(1:3)
expect_set_equal(p$row_ids, 1:150)
expect_set_equal(p2$row_ids, 1:3)
expect_prediction(as_prediction_classif(as.data.table(p2)))
})
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