expect_error(regexp = NA) -> expect_no_condition()

tests/testthat/test-boost_tree_C5.0.R

@@ -13,24 +13,22 @@ test_that('C5.0 execution', {
 
   skip_if_not_installed("C50")
 
-  expect_error(
+  expect_no_condition(
     res <- fit(
       lc_basic,
       Class ~ log(funded_amnt) + int_rate,
       data = lending_club,
       control = ctrl
-    ),
-    regexp = NA
+    )
   )
 
-  expect_error(
+  expect_no_condition(
     res <- fit_xy(
       lc_basic,
       x = lending_club[, num_pred],
       y = lending_club$Class,
       control = ctrl
-    ),
-    regexp = NA
+    )
   )
 
   expect_true(has_multi_predict(res))

tests/testthat/test-boost_tree_xgboost.R

@@ -18,29 +18,25 @@ test_that('xgboost execution, classification', {
   wts <- ifelse(runif(nrow(hpc)) < .1, 0, 1)
   wts <- importance_weights(wts)
 
-  expect_error({
+  expect_no_condition({
     set.seed(1)
     res_f <- parsnip::fit(
       hpc_xgboost,
       class ~ compounds + input_fields,
       data = hpc,
       control = ctrl
     )
-  },
-  regexp = NA
-  )
-  expect_error({
+  })
+  expect_no_condition({
     set.seed(1)
     res_xy <- parsnip::fit_xy(
       hpc_xgboost,
       x = hpc[, c("compounds", "input_fields")],
       y = hpc$class,
       control = ctrl
     )
-  },
-  regexp = NA
-  )
-  expect_error({
+  })
+  expect_no_condition({
     set.seed(1)
     res_f_wts <- parsnip::fit(
       hpc_xgboost,
@@ -49,10 +45,8 @@ test_that('xgboost execution, classification', {
       control = ctrl,
       case_weights = wts
     )
-  },
-  regexp = NA
-  )
-  expect_error({
+  })
+  expect_no_condition({
     set.seed(1)
     res_xy_wts <- parsnip::fit_xy(
       hpc_xgboost,
@@ -61,9 +55,7 @@ test_that('xgboost execution, classification', {
       control = ctrl,
       case_weights = wts
     )
-  },
-  regexp = NA
-  )
+  })
 
   expect_equal(res_f$fit$evaluation_log,     res_xy$fit$evaluation_log)
   expect_equal(res_f_wts$fit$evaluation_log, res_xy_wts$fit$evaluation_log)
@@ -140,24 +132,22 @@ test_that('xgboost execution, regression', {
 
   ctrl$verbosity <- 0L
 
-  expect_error(
+  expect_no_condition(
     res <- parsnip::fit(
       car_basic,
       mpg ~ .,
       data = mtcars,
       control = ctrl
-    ),
-    regexp = NA
+    )
   )
 
-  expect_error(
+  expect_no_condition(
     res <- parsnip::fit_xy(
       car_basic,
       x = mtcars[, num_pred],
       y = mtcars$mpg,
       control = ctrl
-    ),
-    regexp = NA
+    )
   )
 
   expect_error(
@@ -285,32 +275,29 @@ test_that('validation sets', {
 
   ctrl$verbosity <- 0L
 
-  expect_error(
+  expect_no_condition(
     reg_fit <-
       boost_tree(trees = 20, mode = "regression") %>%
       set_engine("xgboost", validation = .1) %>%
-      fit(mpg ~ ., data = mtcars[-(1:4), ]),
-    regex = NA
+      fit(mpg ~ ., data = mtcars[-(1:4), ])
   )
 
   expect_equal(colnames(extract_fit_engine(reg_fit)$evaluation_log)[2], "validation_rmse")
 
-  expect_error(
+  expect_no_condition(
     reg_fit <-
       boost_tree(trees = 20, mode = "regression") %>%
       set_engine("xgboost", validation = .1, eval_metric = "mae") %>%
-      fit(mpg ~ ., data = mtcars[-(1:4), ]),
-    regex = NA
+      fit(mpg ~ ., data = mtcars[-(1:4), ])
   )
 
   expect_equal(colnames(extract_fit_engine(reg_fit)$evaluation_log)[2], "validation_mae")
 
-  expect_error(
+  expect_no_condition(
     reg_fit <-
       boost_tree(trees = 20, mode = "regression") %>%
       set_engine("xgboost", eval_metric = "mae") %>%
-      fit(mpg ~ ., data = mtcars[-(1:4), ]),
-    regex = NA
+      fit(mpg ~ ., data = mtcars[-(1:4), ])
   )
 
   expect_equal(colnames(extract_fit_engine(reg_fit)$evaluation_log)[2], "training_mae")
@@ -334,23 +321,21 @@ test_that('early stopping', {
   ctrl$verbosity <- 0L
 
   set.seed(233456)
-  expect_error(
+  expect_no_condition(
     reg_fit <-
       boost_tree(trees = 200, stop_iter = 5, mode = "regression") %>%
       set_engine("xgboost", validation = .1) %>%
-      fit(mpg ~ ., data = mtcars[-(1:4), ]),
-    regex = NA
+      fit(mpg ~ ., data = mtcars[-(1:4), ])
   )
 
   expect_equal(extract_fit_engine(reg_fit)$niter - extract_fit_engine(reg_fit)$best_iteration, 5)
   expect_true(extract_fit_engine(reg_fit)$niter < 200)
 
-  expect_error(
+  expect_no_condition(
     reg_fit <-
       boost_tree(trees = 20, mode = "regression") %>%
       set_engine("xgboost", validation = .1, eval_metric = "mae") %>%
-      fit(mpg ~ ., data = mtcars[-(1:4), ]),
-    regex = NA
+      fit(mpg ~ ., data = mtcars[-(1:4), ])
   )
 
   expect_warning(
@@ -380,39 +365,39 @@ test_that('xgboost data conversion', {
   mtcar_smat <- Matrix::Matrix(mtcar_mat, sparse = TRUE)
   wts <- 1:32
 
-  expect_error(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg), regexp = NA)
+  expect_no_condition(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg))
   expect_true(inherits(from_df$data, "xgb.DMatrix"))
   expect_true(inherits(from_df$watchlist$training, "xgb.DMatrix"))
 
-  expect_error(from_mat <- parsnip:::as_xgb_data(mtcar_mat, mtcars$mpg), regexp = NA)
+  expect_no_condition(from_mat <- parsnip:::as_xgb_data(mtcar_mat, mtcars$mpg))
   expect_true(inherits(from_mat$data, "xgb.DMatrix"))
   expect_true(inherits(from_mat$watchlist$training, "xgb.DMatrix"))
 
-  expect_error(from_sparse <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg), regexp = NA)
+  expect_no_condition(from_sparse <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg))
   expect_true(inherits(from_mat$data, "xgb.DMatrix"))
   expect_true(inherits(from_mat$watchlist$training, "xgb.DMatrix"))
 
-  expect_error(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, validation = .1), regexp = NA)
+  expect_no_condition(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, validation = .1))
   expect_true(inherits(from_df$data, "xgb.DMatrix"))
   expect_true(inherits(from_df$watchlist$validation, "xgb.DMatrix"))
   expect_true(nrow(from_df$data) > nrow(from_df$watchlist$validation))
 
-  expect_error(from_mat <- parsnip:::as_xgb_data(mtcar_mat, mtcars$mpg, validation = .1), regexp = NA)
+  expect_no_condition(from_mat <- parsnip:::as_xgb_data(mtcar_mat, mtcars$mpg, validation = .1))
   expect_true(inherits(from_mat$data, "xgb.DMatrix"))
   expect_true(inherits(from_mat$watchlist$validation, "xgb.DMatrix"))
   expect_true(nrow(from_mat$data) > nrow(from_mat$watchlist$validation))
 
-  expect_error(from_sparse <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, validation = .1), regexp = NA)
+  expect_no_condition(from_sparse <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, validation = .1))
   expect_true(inherits(from_mat$data, "xgb.DMatrix"))
   expect_true(inherits(from_mat$watchlist$validation, "xgb.DMatrix"))
   expect_true(nrow(from_sparse$data) > nrow(from_sparse$watchlist$validation))
 
   # set event_level for factors
 
   mtcars_y <- factor(mtcars$mpg < 15, levels = c(TRUE, FALSE), labels = c("low", "high"))
-  expect_error(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars_y), regexp = NA)
+  expect_no_condition(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars_y))
   expect_equal(xgboost::getinfo(from_df$data, name = "label")[1:5],  rep(0, 5))
-  expect_error(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars_y, event_level = "second"), regexp = NA)
+  expect_no_condition(from_df <- parsnip:::as_xgb_data(mtcar_x, mtcars_y, event_level = "second"))
   expect_equal(xgboost::getinfo(from_df$data, name = "label")[1:5],  rep(1, 5))
 
   mtcars_y <- factor(mtcars$mpg < 15, levels = c(TRUE, FALSE, "na"), labels = c("low", "high", "missing"))
@@ -421,9 +406,13 @@ test_that('xgboost data conversion', {
   )
 
   # case weights added
-  expect_error(wted <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts), regexp = NA)
+  expect_no_condition(
+    wted <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts)
+  )
   expect_equal(wts, xgboost::getinfo(wted$data, "weight"))
-  expect_error(wted_val <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts, validation = 1/4), regexp = NA)
+  expect_no_condition(
+    wted_val <- parsnip:::as_xgb_data(mtcar_x, mtcars$mpg, weights = wts, validation = 1/4)
+  )
   expect_true(all(xgboost::getinfo(wted_val$data, "weight") %in% wts))
   expect_null(xgboost::getinfo(wted_val$watchlist$validation, "weight"))
 
@@ -461,9 +450,13 @@ test_that('xgboost data and sparse matrices', {
   expect_equal(extract_fit_engine(from_df), extract_fit_engine(from_sparse), ignore_function_env = TRUE)
 
   # case weights added
-  expect_error(wted <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts), regexp = NA)
+  expect_no_condition(
+    wted <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts)
+  )
   expect_equal(wts, xgboost::getinfo(wted$data, "weight"))
-  expect_error(wted_val <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts, validation = 1/4), regexp = NA)
+  expect_no_condition(
+    wted_val <- parsnip:::as_xgb_data(mtcar_smat, mtcars$mpg, weights = wts, validation = 1/4)
+  )
   expect_true(all(xgboost::getinfo(wted_val$data, "weight") %in% wts))
   expect_null(xgboost::getinfo(wted_val$watchlist$validation, "weight"))
 

tests/testthat/test-case-weights.R

@@ -10,22 +10,21 @@ test_that('case weights with xy method', {
   two_class_subset <- two_class_dat[wts != 0, ]
   wts <- importance_weights(wts)
 
-  expect_error({
+  expect_no_condition({
     set.seed(1)
     C5_bst_wt_fit <-
       boost_tree(trees = 5) %>%
       set_engine("C5.0") %>%
       set_mode("classification") %>%
       fit(Class ~ ., data = two_class_dat, case_weights = wts)
-  },
-  regexp = NA)
+  })
 
   expect_output(
     print(C5_bst_wt_fit$fit$call),
     "weights = weights"
   )
 
-  expect_error({
+  expect_no_condition({
     set.seed(1)
     C5_bst_wt_fit <-
       boost_tree(trees = 5) %>%
@@ -36,8 +35,7 @@ test_that('case weights with xy method', {
         y = two_class_dat$Class,
         case_weights = wts
       )
-  },
-  regexp = NA)
+  })
 
   expect_output(
     print(C5_bst_wt_fit$fit$call),
@@ -57,21 +55,20 @@ test_that('case weights with xy method - non-standard argument names', {
   two_class_subset <- two_class_dat[wts != 0, ]
   wts <- importance_weights(wts)
 
-  expect_error({
+  expect_no_condition({
     set.seed(1)
     rf_wt_fit <-
       rand_forest(trees = 5) %>%
       set_mode("classification") %>%
       fit(Class ~ ., data = two_class_dat, case_weights = wts)
-  },
-  regexp = NA)
+  })
 
   # expect_output(
   #   print(rf_wt_fit$fit$call),
   #   "case\\.weights = weights"
   # )
 
-  expect_error({
+  expect_no_condition({
     set.seed(1)
     rf_wt_fit <-
       rand_forest(trees = 5) %>%
@@ -81,8 +78,7 @@ test_that('case weights with xy method - non-standard argument names', {
         y = two_class_dat$Class,
         case_weights = wts
       )
-  },
-  regexp = NA)
+  })
 })
 
 test_that('case weights with formula method', {
@@ -97,11 +93,11 @@ test_that('case weights with formula method', {
   ames_subset <- ames[wts != 0, ]
   wts <- frequency_weights(wts)
 
-  expect_error(
+  expect_no_condition(
     lm_wt_fit <-
       linear_reg() %>%
-      fit(Sale_Price ~ Longitude + Latitude, data = ames, case_weights = wts),
-    regexp = NA)
+      fit(Sale_Price ~ Longitude + Latitude, data = ames, case_weights = wts)
+  )
 
   lm_sub_fit <-
     linear_reg() %>%
@@ -141,15 +137,14 @@ test_that('case weights with formula method that goes through `fit_xy()`', {
   ames_subset <- ames[wts != 0, ]
   wts <- frequency_weights(wts)
 
-  expect_error(
+  expect_no_condition(
     lm_wt_fit <-
       linear_reg() %>%
       fit_xy(
         x = ames[c("Longitude", "Latitude")],
         y = ames$Sale_Price,
         case_weights = wts
-      ),
-    regexp = NA)
+  ))
 
   lm_sub_fit <-
     linear_reg() %>%

tests/testthat/test-descriptors.R

@@ -199,17 +199,15 @@ test_that("can be temporarily overriden at evaluation time", {
 
 test_that("system-level descriptor tests", {
   skip_if_not_installed("xgboost")
-  expect_error(
+  expect_no_condition(
     boost_tree(mode = "regression", mtry = .cols()) %>%
       set_engine("xgboost") %>%
-      fit_xy(x = mtcars[, -1], y = mtcars$mpg),
-    NA
+      fit_xy(x = mtcars[, -1], y = mtcars$mpg)
   )
-  expect_error(
+  expect_no_condition(
     boost_tree(mode = "regression", mtry = .cols()) %>%
       set_engine("xgboost") %>%
-      fit(mpg ~ ., data = mtcars),
-    NA
+      fit(mpg ~ ., data = mtcars)
   )
 
 })