fixed a few test cases

Author: topepo

tests/testthat/test_linear_reg_stan.R

@@ -11,9 +11,9 @@ iris_bad_form <- as.formula(Species ~ term)
 iris_basic <- linear_reg() %>%
   set_engine("stan", seed = 10, chains = 1)
 
-ctrl <- fit_control(verbosity = 0, catch = FALSE)
-caught_ctrl <- fit_control(verbosity = 0, catch = TRUE)
-quiet_ctrl <- fit_control(verbosity = 0, catch = TRUE)
+ctrl <- fit_control(verbosity = 0L, catch = FALSE)
+caught_ctrl <- fit_control(verbosity = 0L, catch = TRUE)
+quiet_ctrl <- fit_control(verbosity = 0L, catch = TRUE)
 
 # ------------------------------------------------------------------------------
 

tests/testthat/test_logistic_reg_stan.R

@@ -29,14 +29,12 @@ test_that('stan_glm execution', {
       lc_basic,
       funded_amnt ~ term,
       data = lending_club,
-      engine = "stan",
       control = ctrl
     )
   )
 
   stan_xy_catch <- fit_xy(
     lc_basic,
-    engine = "stan",
     control = caught_ctrl,
     x = lending_club[, num_pred],
     y = lending_club$total_bal_il
@@ -53,7 +51,6 @@ test_that('stan_glm prediction', {
   xy_fit <- fit_xy(
     logistic_reg() %>%
       set_engine("stan", seed = 1333, chains = 1),
-    engine = "stan",
     control = ctrl,
     x = lending_club[, num_pred],
     y = lending_club$Class
@@ -74,7 +71,6 @@ test_that('stan_glm prediction', {
       set_engine("stan", seed = 1333, chains = 1),
     Class ~ log(funded_amnt) + int_rate,
     data = lending_club,
-    engine = "stan",
     control = ctrl
   )
 
@@ -96,7 +92,6 @@ test_that('stan_glm probability', {
   xy_fit <- fit_xy(
     logistic_reg() %>%
       set_engine("stan", seed = 1333, chains = 1),
-    engine = "stan",
     control = ctrl,
     x = lending_club[, num_pred],
     y = lending_club$Class
@@ -115,7 +110,6 @@ test_that('stan_glm probability', {
       set_engine("stan", seed = 1333, chains = 1),
     Class ~ log(funded_amnt) + int_rate,
     data = lending_club,
-    engine = "stan",
     control = ctrl
   )
 
@@ -137,7 +131,6 @@ test_that('stan intervals', {
       set_engine("stan", seed = 1333, chains = 1),
     Class ~ log(funded_amnt) + int_rate,
     data = lending_club,
-    engine = "stan",
     control = ctrl
   )
 

tests/testthat/test_mlp_keras.R

@@ -9,8 +9,8 @@ context("simple neural network execution with keras")
 num_pred <- names(iris)[1:4]
 
 iris_keras <-
-  mlp(mode = "classification", hidden_units = 2) %>%
-  set_engine("keras", verbose = 0, epochs = 10)
+  mlp(mode = "classification", hidden_units = 2, epochs = 10) %>%
+  set_engine("keras", verbose = 0)
 
 ctrl <- fit_control(verbosity = 1, catch = FALSE)
 caught_ctrl <- fit_control(verbosity = 1, catch = TRUE)
@@ -130,8 +130,8 @@ mtcars <- as.data.frame(scale(mtcars))
 
 num_pred <- names(mtcars)[3:6]
 
-car_basic <- mlp(mode = "regression") %>%
-  set_engine("keras", verbose = 0, epochs = 10)
+car_basic <- mlp(mode = "regression", epochs = 10) %>%
+  set_engine("keras", verbose = 0)
 
 bad_keras_reg <-
   mlp(mode = "regression") %>%
@@ -176,8 +176,8 @@ test_that('keras regression prediction', {
   skip_if_not_installed("keras")
 
   xy_fit <- parsnip::fit_xy(
-    mlp(mode = "regression", hidden_units = 2) %>%
-      set_engine("keras", epochs = 500, penalty = .1, verbose = 0),
+    mlp(mode = "regression", hidden_units = 2, epochs = 500, penalty = .1) %>%
+      set_engine("keras", verbose = 0),
     x = mtcars[, c("cyl", "disp")],
     y = mtcars$mpg,
     control = ctrl
@@ -211,8 +211,8 @@ test_that('multivariate nnet formula', {
   skip_if_not_installed("keras")
 
   nnet_form <-
-    mlp(mode = "regression", hidden_units = 3)  %>%
-    set_engine("keras", penalty = 0.01, verbose = 0) %>%
+    mlp(mode = "regression", hidden_units = 3, penalty = 0.01)  %>%
+    set_engine("keras", verbose = 0) %>%
     parsnip::fit(
       cbind(V1, V2, V3) ~ .,
       data = nn_dat[-(1:5),]
@@ -226,8 +226,8 @@ test_that('multivariate nnet formula', {
   keras::backend()$clear_session()
 
   nnet_xy <-
-    mlp(mode = "regression", hidden_units = 3)  %>%
-    set_engine("keras", penalty = 0.01, verbose = 0) %>%
+    mlp(mode = "regression", hidden_units = 3, penalty = 0.01)  %>%
+    set_engine("keras", verbose = 0) %>%
     parsnip::fit_xy(
       x = nn_dat[-(1:5), -(1:3)],
       y = nn_dat[-(1:5),   1:3 ]