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test-predict-multiclass.R
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test-predict-multiclass.R
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set.seed(245)
xdat <- data.frame(
yint = sample(c(0L, 1L, 2L), 100, replace = TRUE),
yfact = factor(sample(c("hi", "mid", "lo"), 100, replace = TRUE)),
ychar = sample(c("hi", "mid", "lo"), 100, replace = TRUE),
x1 = rnorm(100),
x2 = rnorm(100),
x3 = cut(runif(100), 3, labels = 1:3),
x4 = cut(runif(100), 2, labels = 1:2)
)
test_that("Default: L2 with 'prob'", {
classif_fit <- rpf(yfact ~ ., data = xdat)
expect_identical(classif_fit$loss, "L2")
classif_pred <- predict(classif_fit, xdat)
classif_pred_prob <- predict(classif_fit, new_data = xdat, type = "prob")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gte(min(classif_pred), 0)
expect_lte(min(classif_pred), 1)
expect_identical(classif_pred, classif_pred_prob)
})
# L2 loss -----------------------------------------------------------------
test_that("L2: Probability prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L2")
classif_pred <- predict(classif_fit, new_data = xdat, type = "prob")
expect_identical(classif_fit$loss, "L2")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gte(min(classif_pred), 0)
expect_lte(max(classif_pred), 1)
})
test_that("L2: Class prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L2")
classif_pred <- predict(classif_fit, new_data = xdat, type = "class")
expect_identical(classif_fit$loss, "L2")
expect_equal(dim(classif_pred), c(nrow(xdat), 1))
expect_true(all(classif_pred$.pred_class %in% levels(xdat$yfact)))
})
test_that("L2: Numeric prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L2")
classif_pred <- predict(classif_fit, new_data = xdat, type = "numeric")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gt(max(classif_pred), 1)
expect_lt(min(classif_pred), 0)
})
# L1 loss -----------------------------------------------------------------
test_that("L1: Probability prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L1")
classif_pred <- predict(classif_fit, new_data = xdat, type = "prob")
expect_identical(classif_fit$loss, "L1")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gte(min(classif_pred), 0)
expect_lte(max(classif_pred), 1)
})
test_that("L1: Class prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L1")
classif_pred <- predict(classif_fit, new_data = xdat, type = "class")
expect_identical(classif_fit$loss, "L1")
expect_equal(dim(classif_pred), c(nrow(xdat), 1))
expect_true(all(classif_pred$.pred_class %in% levels(xdat$yfact)))
})
test_that("L1: Numeric prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "L1")
classif_pred <- predict(classif_fit, new_data = xdat, type = "numeric")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
# Hard to test for the possibility of pred > 1 | pred < 0 w/o fixed data
# expect_lte(min(classif_pred), 0)
# expect_gte(max(classif_pred), 1)
})
# logit loss --------------------------------------------------------------
test_that("logit: Probability prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "logit")
classif_pred <- predict(classif_fit, new_data = xdat, type = "prob")
expect_identical(classif_fit$loss, "logit")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gte(min(classif_pred), 0)
expect_lte(max(classif_pred), 1)
})
test_that("logit: Class prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "logit")
classif_pred <- predict(classif_fit, new_data = xdat, type = "class")
expect_equal(dim(classif_pred), c(nrow(xdat), 1))
expect_true(all(classif_pred$.pred_class %in% levels(xdat$yfact)))
})
test_that("logit: Numeric/link prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "logit")
classif_pred <- predict(classif_fit, new_data = xdat, type = "numeric")
classif_pred_lnk <- predict(classif_fit, new_data = xdat, type = "link")
expect_identical(classif_pred, classif_pred_lnk)
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
})
# exponential loss --------------------------------------------------------
test_that("exponential: Probability prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "exponential")
classif_pred <- predict(classif_fit, new_data = xdat, type = "prob")
expect_identical(classif_fit$loss, "exponential_2")
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
expect_gte(min(classif_pred), 0)
expect_lte(max(classif_pred), 1)
})
test_that("exponential: Class prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "exponential")
classif_pred <- predict(classif_fit, new_data = xdat, type = "class")
expect_equal(dim(classif_pred), c(nrow(xdat), 1))
expect_true(all(classif_pred$.pred_class %in% levels(xdat$yfact)))
})
test_that("exponential: Numeric/link prediction", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "exponential")
classif_pred <- predict(classif_fit, new_data = xdat, type = "numeric")
classif_pred_lnk <- predict(classif_fit, new_data = xdat, type = "link")
expect_identical(classif_pred, classif_pred_lnk)
expect_equal(dim(classif_pred), c(nrow(xdat), nlevels(xdat$yfact)))
})
# Classif and prob agree with each other ----------------------------------
test_that("prob and classif yield same result", {
classif_fit <- rpf(yfact ~ ., data = xdat, loss = "logit")
pred_class <- predict(classif_fit, new_data = xdat, type = "class")
pred_prob <- predict(classif_fit, new_data = xdat, type = "prob")
pred_max <- apply(pred_prob, 1, which.max)
pred_prob$pred_prob_class <- factor(
levels(xdat$yfact)[pred_max],
levels = levels(xdat$yfact)
)
pred_both <- cbind(pred_prob, pred_class)
expect_equal(pred_both$pred_prob_class, pred_both$.pred_class)
})