use capture.output in more places

tests/testthat/test-fit-init.R

@@ -5,6 +5,7 @@ set_cmdstan_path()
 data_list_schools <- testing_data("schools")
 data_list_logistic <- testing_data("logistic")
 test_inits <- function(mod, fit_init, data_list = NULL) {
+ utils::capture.output({
   fit_sample <- mod$sample(data = data_list, chains = 1, init = fit_init,
     iter_sampling = 100, iter_warmup = 100, refresh = 0, seed = 1234)
   fit_sample_multi <- mod$sample(data = data_list, chains = 5, init = fit_init,
@@ -20,6 +21,7 @@ test_inits <- function(mod, fit_init, data_list = NULL) {
   draws = posterior::as_draws_rvars(fit_init$draws())
   fit_sample_draws <- mod$sample(data = data_list, chains = 1, init = draws,
     iter_sampling = 100, iter_warmup = 100, refresh = 0, seed = 1234)
+ })
   return(0)
 }
 

tests/testthat/test-fit-shared.R

@@ -454,22 +454,26 @@ test_that("draws are returned for model with spaces", {
 test_that("sampling with inits works with include_paths", {
   stan_program_w_include <- testing_stan_file("bernoulli_include")
   exe <- cmdstan_ext(strip_ext(stan_program_w_include))
-  if(file.exists(exe)) {
+  if (file.exists(exe)) {
     file.remove(exe)
   }
 
-    mod_w_include <- cmdstan_model(stan_file = stan_program_w_include, quiet = FALSE,
-                                   include_paths = test_path("resources", "stan"))
+  mod_w_include <- cmdstan_model(stan_file = stan_program_w_include,
+                                 include_paths = test_path("resources", "stan"))
 
   data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
-
-  fit <- mod_w_include$sample(
-    data = data_list,
-    seed = 123,
-    chains = 4,
-    parallel_chains = 4,
-    refresh = 500,
-    init = list(list(theta = 0.25), list(theta = 0.25), list(theta = 0.25), list(theta = 0.25))
+  expect_no_error(
+    fit <- mod_w_include$sample(
+      data = data_list,
+      seed = 123,
+      chains = 4,
+      parallel_chains = 4,
+      refresh = 500,
+      init = list(list(theta = 0.25),
+                  list(theta = 0.25),
+                  list(theta = 0.25),
+                  list(theta = 0.25))
+    )
   )
 })
 
@@ -548,8 +552,12 @@ test_that("code() warns if model not created with Stan file", {
   stan_program <- testing_stan_file("bernoulli")
   mod <- testing_model("bernoulli")
   mod_exe <- cmdstan_model(exe_file = mod$exe_file())
-  fit_exe <- mod_exe$sample(data = list(N = 10, y = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1)),
-                            refresh = 0)
+  utils::capture.output(
+    fit_exe <- mod_exe$sample(
+      data = list(N = 10, y = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1)),
+      refresh = 0
+    )
+  )
   expect_warning(
     expect_null(fit_exe$code()),
     "'$code()' will return NULL because the 'CmdStanModel' was not created with a Stan file",

tests/testthat/test-model-expose-functions.R

@@ -80,11 +80,13 @@ stan_prog <- paste(function_decl,
 model <- write_stan_file(stan_prog)
 data_list <- testing_data("bernoulli")
 mod <- cmdstan_model(model, force_recompile = TRUE)
-fit <- mod$sample(data = data_list)
+utils::capture.output(
+  fit <- mod$sample(data = data_list)
+)
 
 
 test_that("Functions can be exposed in model object", {
-  expect_no_error(mod$expose_functions(verbose = TRUE))
+  expect_no_error(mod$expose_functions())
 })
 
 
@@ -260,7 +262,7 @@ test_that("Functions handle complex types correctly", {
 })
 
 test_that("Functions can be exposed in fit object", {
-  fit$expose_functions(verbose = TRUE)
+  fit$expose_functions()
 
   expect_equal(
     fit$functions$rtn_vec(c(1,2,3,4)),
@@ -284,7 +286,9 @@ test_that("Compiled functions can be copied to global environment", {
 
 test_that("Functions can be compiled with model", {
   mod <- cmdstan_model(model, force_recompile = TRUE, compile_standalone = TRUE)
-  fit <- mod$sample(data = data_list)
+  utils::capture.output(
+    fit <- mod$sample(data = data_list)
+  )
 
   expect_message(
     fit$expose_functions(),
@@ -344,9 +348,11 @@ test_that("rng functions can be exposed", {
   model <- write_stan_file(stan_prog)
   data_list <- testing_data("bernoulli")
   mod <- cmdstan_model(model, force_recompile = TRUE)
-  fit <- mod$sample(data = data_list)
+  utils::capture.output(
+    fit <- mod$sample(data = data_list)
+  )
 
-  fit$expose_functions(verbose = TRUE)
+  fit$expose_functions()
   set.seed(10)
   res1_1 <- fit$functions$wrap_normal_rng(5,10)
   res2_1 <- fit$functions$wrap_normal_rng(5,10)

tests/testthat/test-model-init.R

@@ -303,10 +303,12 @@ test_that("Initial values for single-element containers treated correctly", {
   "
   mod <- cmdstan_model(write_stan_file(modcode), force_recompile = TRUE)
   expect_no_error(
-    fit <- mod$sample(
-      data = list(y_mean = 0),
-      init = list(list(y = c(0))),
-      chains = 1
+    utils::capture.output(
+      fit <- mod$sample(
+        data = list(y_mean = 0),
+        init = list(list(y = c(0))),
+        chains = 1
+      )
     )
   )
 })
@@ -331,7 +333,13 @@ test_that("Pathfinder inits do not drop dimensions", {
   "
   mod <- cmdstan_model(write_stan_file(modcode), force_recompile = TRUE)
   data <- list(N = 100, y = rnorm(100))
-  pf <- mod$pathfinder(data = data, psis_resample = FALSE)
-  expect_no_error(fit <- mod$sample(data = data, init = pf, chains = 1,
-                                    iter_warmup = 100, iter_sampling = 100))
-})
+  utils::capture.output(
+    pf <- mod$pathfinder(data = data, psis_resample = FALSE)
+  )
+  expect_no_error(
+    utils::capture.output(
+      fit <- mod$sample(data = data, init = pf, chains = 1,
+                        iter_warmup = 100, iter_sampling = 100)
+    )
+  )
+})

tests/testthat/test-model-laplace.R

@@ -63,10 +63,12 @@ test_that("laplace() runs when all arguments specified validly", {
 })
 
 test_that("laplace() all valid 'mode' inputs give same results", {
-  mode <- mod$optimize(data = data_list, jacobian = TRUE, seed = 100, refresh = 0)
-  fit1 <- mod$laplace(data = data_list, mode = mode, seed = 100, refresh = 0)
-  fit2 <- mod$laplace(data = data_list, mode = mode$output_files(), seed = 100, refresh = 0)
-  fit3 <- mod$laplace(data = data_list, mode = NULL, seed = 100, refresh = 0)
+  utils::capture.output({
+    mode <- mod$optimize(data = data_list, jacobian = TRUE, seed = 100, refresh = 0)
+    fit1 <- mod$laplace(data = data_list, mode = mode, seed = 100, refresh = 0)
+    fit2 <- mod$laplace(data = data_list, mode = mode$output_files(), seed = 100, refresh = 0)
+    fit3 <- mod$laplace(data = data_list, mode = NULL, seed = 100, refresh = 0)
+  })
 
   expect_is(fit1, "CmdStanLaplace")
   expect_is(fit2, "CmdStanLaplace")
@@ -85,17 +87,22 @@ test_that("laplace() all valid 'mode' inputs give same results", {
 })
 
 test_that("laplace() allows choosing number of draws", {
-  fit <- mod$laplace(data = data_list, draws = 10, refresh = 0)
+  utils::capture.output({
+    fit <- mod$laplace(data = data_list, draws = 10, refresh = 0)
+    fit2 <- mod$laplace(data = data_list, draws = 100, refresh = 0)
+  })
+
   expect_equal(fit$metadata()$draws, 10)
   expect_equal(posterior::ndraws(fit$draws()), 10)
 
-  fit2 <- mod$laplace(data = data_list, draws = 100, refresh = 0)
   expect_equal(fit2$metadata()$draws, 100)
   expect_equal(posterior::ndraws(fit2$draws()), 100)
 })
 
 test_that("laplace() errors if jacobian arg doesn't match what optimize used", {
-  fit <- mod$optimize(data = data_list, jacobian = FALSE, refresh = 0)
+  utils::capture.output(
+    fit <- mod$optimize(data = data_list, jacobian = FALSE, refresh = 0)
+  )
   expect_error(
     mod$laplace(data = data_list, mode = fit, jacobian = TRUE),
     "'jacobian' argument to optimize and laplace must match"
@@ -107,7 +114,9 @@ test_that("laplace() errors if jacobian arg doesn't match what optimize used", {
 })
 
 test_that("laplace() errors with bad combinations of arguments", {
-  fit <- mod$optimize(data = data_list, jacobian = TRUE, refresh = 0)
+  utils::capture.output(
+    fit <- mod$optimize(data = data_list, jacobian = TRUE, refresh = 0)
+  )
   expect_error(
     mod$laplace(data = data_list, mode = mod, opt_args = list(iter = 10)),
     "Cannot specify both 'opt_args' and 'mode' arguments."

tests/testthat/test-model-methods.R

@@ -4,7 +4,9 @@ skip_if(os_is_wsl())
 set_cmdstan_path()
 mod <- cmdstan_model(testing_stan_file("bernoulli_log_lik"), force_recompile = TRUE)
 data_list <- testing_data("bernoulli")
-fit <- mod$sample(data = data_list, chains = 1, refresh = 0)
+utils::capture.output(
+  fit <- mod$sample(data = data_list, chains = 1, refresh = 0)
+)
 
 test_that("Model methods automatically initialise when needed", {
   expect_no_error(fit$log_prob(unconstrained_variables=c(0.1)))
@@ -59,7 +61,9 @@ test_that("Model methods environments are independent", {
   data_list_2 <- data_list
   data_list_2$N <- 20
   data_list_2$y <- c(data_list$y, data_list$y)
-  fit_2 <- mod$sample(data = data_list_2, chains = 1)
+  utils::capture.output(
+    fit_2 <- mod$sample(data = data_list_2, chains = 1)
+  )
   fit_2$init_model_methods()
 
   expect_equal(fit$log_prob(unconstrained_variables=c(0.1)), -8.6327599208828509347)
@@ -90,8 +94,10 @@ test_that("methods error for incorrect inputs", {
 
   logistic_mod <- cmdstan_model(testing_stan_file("logistic"), force_recompile = TRUE)
   logistic_data_list <- testing_data("logistic")
-  logistic_fit <- logistic_mod$sample(data = logistic_data_list, chains = 1)
-  logistic_fit$init_model_methods(verbose = TRUE)
+  utils::capture.output(
+    logistic_fit <- logistic_mod$sample(data = logistic_data_list, chains = 1)
+  )
+  logistic_fit$init_model_methods()
 
   expect_error(
     logistic_fit$unconstrain_variables(list(alpha = 0.5)),
@@ -104,7 +110,9 @@ test_that("Methods error with already-compiled model", {
   precompile_mod <- testing_model("bernoulli")
   mod <- testing_model("bernoulli")
   data_list <- testing_data("bernoulli")
-  fit <- mod$sample(data = data_list, chains = 1)
+  utils::capture.output(
+    fit <- mod$sample(data = data_list, chains = 1)
+  )
   expect_error(
     fit$init_model_methods(),
     "Model methods cannot be used with a pre-compiled Stan executable, the model must be compiled again",
@@ -116,7 +124,9 @@ test_that("Methods can be compiled with model", {
   mod <- cmdstan_model(testing_stan_file("bernoulli"),
                        force_recompile = TRUE,
                        compile_model_methods = TRUE)
-  fit <- mod$sample(data = data_list, chains = 1)
+  utils::capture.output(
+    fit <- mod$sample(data = data_list, chains = 1)
+  )
 
   lp <- fit$log_prob(unconstrained_variables=c(0.6))
   expect_equal(lp, -10.649855405830624733)
@@ -156,7 +166,9 @@ test_that("unconstrain_variables correctly handles zero-length containers", {
   mod <- cmdstan_model(write_stan_file(model_code),
                        force_recompile = TRUE,
                        compile_model_methods = TRUE)
-  fit <- mod$sample(data = list(N = 0), chains = 1)
+  utils::capture.output(
+    fit <- mod$sample(data = list(N = 0), chains = 1)
+  )
   unconstrained <- fit$unconstrain_variables(variables = list(x = 5))
   expect_equal(unconstrained, 5)
 })
@@ -179,21 +191,23 @@ test_that("unconstrain_draws returns correct values", {
   mod <- cmdstan_model(write_stan_file(model_code),
                        compile_model_methods = TRUE,
                        force_recompile = TRUE)
-  fit <- mod$sample(data = list(N = 0), chains = 2, save_warmup = TRUE)
-  fit_no_warmup <- mod$sample(data = list(N = 0), chains = 2)
+  utils::capture.output({
+    fit <- mod$sample(data = list(N = 0), chains = 2, save_warmup = TRUE)
+    fit_no_warmup <- mod$sample(data = list(N = 0), chains = 2)
+  })
 
   x_draws <- fit$draws(format = "draws_df")$x
   x_draws_warmup <- fit$draws(format = "draws_df", inc_warmup = TRUE)$x
-  
+
   # Unconstrain all internal draws
   unconstrained_internal_draws <- fit$unconstrain_draws()
   unconstrained_internal_draws_warmup <- fit$unconstrain_draws(inc_warmup = TRUE)
   expect_equal(as.numeric(x_draws), as.numeric(unconstrained_internal_draws))
   expect_equal(as.numeric(x_draws_warmup), as.numeric(unconstrained_internal_draws_warmup))
-  
+
   expect_error({unconstrained_internal_draws <- fit_no_warmup$unconstrain_draws(inc_warmup = TRUE)},
                "Warmup draws were requested from a fit object without them! Please rerun the model with save_warmup = TRUE.")
-  
+
   # Unconstrain external CmdStan CSV files
   unconstrained_csv <- fit$unconstrain_draws(files = fit$output_files())
   unconstrained_csv_warmup <- fit$unconstrain_draws(files = fit$output_files(),
@@ -204,7 +218,7 @@ test_that("unconstrain_draws returns correct values", {
   # Unconstrain existing draws object
   unconstrained_draws <- fit$unconstrain_draws(draws = fit$draws())
   expect_equal(as.numeric(x_draws), as.numeric(unconstrained_draws))
-  
+
   expect_message(fit$unconstrain_draws(draws = fit$draws(), inc_warmup = TRUE),
                  "'inc_warmup' cannot be used with a draws object. Ignoring.")
 
@@ -224,7 +238,9 @@ test_that("unconstrain_draws returns correct values", {
   mod <- cmdstan_model(write_stan_file(model_code),
                        compile_model_methods = TRUE,
                        force_recompile = TRUE)
-  fit <- mod$sample(data = list(N = 0), chains = 2)
+  utils::capture.output(
+    fit <- mod$sample(data = list(N = 0), chains = 2)
+  )
 
   x_draws <- fit$draws(format = "draws_df")$x
 
@@ -241,10 +257,13 @@ test_that("unconstrain_draws returns correct values", {
 })
 
 test_that("Model methods can be initialised for models with no data", {
-
   stan_file <- write_stan_file("parameters { real x; } model { x ~ std_normal(); }")
   mod <- cmdstan_model(stan_file, compile_model_methods = TRUE, force_recompile = TRUE)
-  expect_no_error(fit <- mod$sample())
+  expect_no_error(
+    utils::capture.output(
+      fit <- mod$sample()
+    )
+  )
   expect_equal(fit$log_prob(5), -12.5)
 })
 
@@ -268,8 +287,10 @@ test_that("Variable skeleton returns correct dimensions for matrices", {
                       force_recompile = TRUE)
   N <- 4
   K <- 3
-  fit <- mod$sample(data = list(N = N, K = K), chains = 1,
-                    iter_warmup = 1, iter_sampling = 5)
+  utils::capture.output(
+    fit <- mod$sample(data = list(N = N, K = K), chains = 1,
+                      iter_warmup = 1, iter_sampling = 5)
+  )
 
   target_skeleton <- list(
     x_real = array(0, dim = 1),

tests/testthat/test-model-optimize.R

@@ -146,9 +146,10 @@ test_that("optimize() method runs when the stan file is removed", {
 })
 
 test_that("optimize() recognizes new jacobian argument", {
-  fit <- mod$optimize(data = data_list, jacobian = FALSE)
+  utils::capture.output({
+    fit <- mod$optimize(data = data_list, jacobian = FALSE)
+    fit2 <- mod$optimize(data = data_list, jacobian = TRUE)
+  })
   expect_equal(fit$metadata()$jacobian, 0)
-
-  fit2 <- mod$optimize(data = data_list, jacobian = TRUE)
   expect_equal(fit2$metadata()$jacobian, 1)
 })

tests/testthat/test-model-sample.R

@@ -350,15 +350,17 @@ test_that("Errors are suppressed with show_exceptions", {
   "
   errmod <- cmdstan_model(write_stan_file(errmodcode), force_recompile = TRUE)
 
-  expect_message(
-    suppressWarnings(errmod$sample(data = list(y_mean = 1), chains = 1)),
-    "Chain 1 Exception: vector[uni] assign: accessing element out of range",
-    fixed = TRUE
-  )
+  expect_sample_output(
+    expect_message(
+      suppressWarnings(errmod$sample(data = list(y_mean = 1), chains = 1)),
+      "Chain 1 Exception: vector[uni] assign: accessing element out of range",
+      fixed = TRUE
+    ))
 
-  expect_no_message(
-    suppressWarnings(errmod$sample(data = list(y_mean = 1), chains = 1, show_exceptions = FALSE))
-  )
+  expect_sample_output(
+    expect_no_message(
+      suppressWarnings(errmod$sample(data = list(y_mean = 1), chains = 1, show_exceptions = FALSE))
+    ))
 })
 
 test_that("All output can be suppressed by show_messages", {