Merge pull request #16 from ethanwhite/test-updates

Use r-lib actions for install and check

.github/workflows/test-package.yml

@@ -37,10 +37,6 @@ jobs:
         run: |
           reticulate::install_miniconda()
           reticulate::py_install('DeepForest', pip=TRUE)
-          install.packages('deepforestr')
         shell: Rscript {0}
-      - name: Check
-        run: |
-          options(crayon.enabled = TRUE)
-          rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
-        shell: Rscript {0}
+      - name: Build and check deepforester
+        uses: r-lib/actions/check-r-package@v2

tests/testthat/test-integration.R

@@ -2,47 +2,33 @@ library(testthat)
 
 context("integration tests for DeepForest in R")
 
-test_that("deepforest model is installed", {
-    
-    # Python package not installed on CRAN
-
-    skip_on_cran() 
+skip_if_no_deepforest <- function() {
+  deepforest_avail <- reticulate::py_module_available("deepforest")
+  if (!deepforest_avail)
+    skip("deepforest not available for testing")
+}
 
+test_that("deepforest model is installed", {
+  skip_if_no_deepforest()
   deepforest_available = reticulate::py_module_available("deepforest")
   expect_identical(deepforest_available, TRUE)
 })
 
 test_that("deepforest model exists when loaded", {
-
-  # Python package not installed on CRAN
-
-    skip_on_cran() 
-
+  skip_if_no_deepforest()
   model = df_model()
   expect_type(model, "closure")
-
 })
 
 test_that("use_release model exists when loaded", {
-
-  # Python package not installed on CRAN
-
-    skip_on_cran() 
-
+  skip_if_no_deepforest()
   model = df_model()
   model$use_release()
   expect_type(model, "closure")
-
 })
 
 test_that("image prediction works", {
-
-  # Python package not installed on CRAN
-
-    skip_on_cran() 
-
-  expect
-
+  skip_if_no_deepforest()
   model = df_model()
   model$use_release()
   image_path = get_data("OSBS_029.png")
@@ -51,9 +37,7 @@ test_that("image prediction works", {
 })
 
 test_that("training works", {
-
-  skip_on_cran()
-
+  skip_if_no_deepforest()
   model = df_model()
   model$use_release()
   annotations_file = get_data("testfile_deepforest.csv")
@@ -65,4 +49,4 @@ test_that("training works", {
   model$create_trainer()
   model$trainer$fit(model)
   expect_type(model, "closure")
-})
+})