TST only test nnet_softmax

Author: patr1ckm

.travis.yml

@@ -52,7 +52,6 @@ before_install:
   - sudo apt-get -y install python3
   - mkdir -p ~/.R && echo "CXX14=g++-6" > ~/.R/Makevars
   - echo "CXX14FLAGS += -fPIC" >> ~/.R/Makevars
-  - Rscript -e 'install.packages("nnet")'
 
 
 after_success:

tests/testthat/test_mlp.R

@@ -157,3 +157,11 @@ test_that('bad input', {
   expect_error(translate(mlp(mode = "regression", formula = y ~ x) %>% set_engine()))
 })
 
+test_that("nnet_softmax", {
+  obj <- mlp(mode = 'classification')
+  obj$lvls <- c("a", "b")
+  res <- nnet_softmax(matrix(c(.8, .2)), obj)
+  expect_equal(names(res), obj$lvls)
+  expect_equal(res$b, 1 - res$a)
+})
+

tests/testthat/test_predict_formats.R

@@ -28,12 +28,6 @@ lr_fit_2 <-
   set_engine("glm") %>%
   fit(Ozone ~ ., data = class_dat2)
 
-lr_fit_3 <-
-  mlp(mode = 'classification') %>%
-  set_engine("nnet") %>%
-  fit(Ozone ~ ., data = class_dat2[1:5, ])
-
-
 # ------------------------------------------------------------------------------
 
 test_that('regression predictions', {
@@ -60,11 +54,8 @@ test_that('non-standard levels', {
 
   expect_true(is_tibble(predict(lr_fit_2, new_data = class_dat2[1:5,-1], type = "prob")))
   expect_true(is_tibble(parsnip:::predict_classprob.model_fit(lr_fit_2, new_data = class_dat2[1:5,-1])))
-  final_colnames <- c(".pred_2low", ".pred_high+values")
   expect_equal(names(predict(lr_fit_2, new_data = class_dat2[1:5,-1], type = "prob")),
-               final_colnames)
-  expect_equal(names(predict(lr_fit_3, new_data = class_dat2, type = 'prob')),
-               final_colnames)
+               c(".pred_2low", ".pred_high+values"))
   expect_equal(names(parsnip:::predict_classprob.model_fit(lr_fit_2, new_data = class_dat2[1:5,-1])),
                c("2low", "high+values"))
 })