add some skip_if_not_installed, fix readme

giuseppec · Feb 24, 2025 · 2fe0d6a · 2fe0d6a
1 parent 0f0d444
commit 2fe0d6a
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diff --git a/README.Rmd b/README.Rmd
@@ -4,11 +4,11 @@ output: github_document
 
 <!-- badges: start -->
 
-[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r.yml/badge.svg)](https://github.com/giuseppec/iml/actions)
+[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/giuseppec/iml/actions)
 [![CRAN Status
 Badge](https://www.r-pkg.org/badges/version-ago/iml)](https://CRAN.R-project.org/package=iml)
 [![CRAN
-Downloads](http://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)
+Downloads](https://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)
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diff --git a/README.md b/README.md
@@ -1,11 +1,11 @@
 
 <!-- badges: start -->
 
-[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r.yml/badge.svg)](https://github.com/giuseppec/iml/actions)
+[![r-cmd-check](https://github.com/giuseppec/iml/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/giuseppec/iml/actions)
 [![CRAN Status
 Badge](https://www.r-pkg.org/badges/version-ago/iml)](https://CRAN.R-project.org/package=iml)
 [![CRAN
-Downloads](http://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)
+Downloads](https://cranlogs.r-pkg.org/badges/grand-total/iml)](https://CRAN.R-project.org/package=iml)
 [![codecov.io](https://codecov.io/github/christophM/iml/coverage.svg?branch=master)](https://codecov.io/github/christophM/iml?branch=master)
 [![DOI](http://joss.theoj.org/papers/10.21105/joss.00786/status.svg)](https://doi.org/10.21105/joss.00786)
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diff --git a/tests/testthat/test-FeatureEffect.R b/tests/testthat/test-FeatureEffect.R
@@ -537,6 +537,7 @@ test_that("method='ale' works for 2D numerical", {
 })
 
 test_that("iml::FeatureEffect with method='ale' equal to ALEPLot::ALEPlot", {
+  skip_if_not_installed("ALEPlot")
   require("ALEPlot")
 
   # one numerical feature

diff --git a/tests/testthat/test-FeatureImp.R b/tests/testthat/test-FeatureImp.R
@@ -49,6 +49,7 @@ test_that("FeatureImp with difference", {
 })
 
 test_that("FeatureImp with 0 model error", {
+  skip_if_not_installed("mlr")
   data(iris)
   require("mlr")
   lrn <- mlr::makeLearner("classif.rpart", predict.type = "prob")
@@ -110,8 +111,10 @@ test_that("Works for different repetitions.", {
 
 test_that("Model receives data.frame without additional columns", {
   # https://stackoverflow.com/questions/51980808/r-plotting-importance-feature-using-featureimpnew
-  library(mlr)
-  library(ranger)
+  skip_if_not_installed("mlr")
+  skip_if_not_installed("ranger")
+  require(mlr)
+  require(ranger)
   data("iris")
   tsk <- mlr::makeClassifTask(data = iris, target = "Species")
   lrn <- mlr::makeLearner("classif.ranger", predict.type = "prob")

diff --git a/tests/testthat/test-LocalModel.R b/tests/testthat/test-LocalModel.R
@@ -40,7 +40,8 @@ test_that("LocalModel works for single output and single feature", {
 })
 
 test_that("LocalModel works for multiple output", {
-  library("rpart")
+  skip_if_not_installed("rpart")
+  require("rpart")
   clf <- rpart(Species ~ ., data = iris)
   mod <- Predictor$new(clf, data = iris)
   x.interest <- iris[1, -5]

diff --git a/tests/testthat/test-MarginalGenerator.R b/tests/testthat/test-MarginalGenerator.R
@@ -1,3 +1,4 @@
+# skip_if_not_installed("data.table")
 library(data.table)
 mg.dat <- data.table(
   a = 1:10,

diff --git a/tests/testthat/test-Predictor.R b/tests/testthat/test-Predictor.R
@@ -158,6 +158,7 @@ test_that("equivalence", {
 })
 
 test_that("Missing predict.type for mlr gives warning", {
+  skip_if_not_installed("mlr")
   task <- mlr::makeClassifTask(data = iris, target = "Species")
   lrn <- mlr::makeLearner("classif.randomForest")
   mod.mlr <- mlr::train(lrn, task)
@@ -166,12 +167,14 @@ test_that("Missing predict.type for mlr gives warning", {
 
 # Test numeric predictions
 
+skip_if_not_installed("mlr")
 ## mlr
 task <- mlr::makeRegrTask(data = Boston, target = "medv")
 lrn <- mlr::makeLearner("regr.rpart")
 mod.mlr <- mlr::train(lrn, task)
 predictor.mlr <- Predictor$new(mod.mlr, data = Boston)
 
+skip_if_not_installed("mlr3")
 # mlr3
 task <- TaskRegr$new(id = "bn", backend = Boston, target = "medv")
 learner <- lrn("regr.rpart")
@@ -182,6 +185,7 @@ predictor.mlr3 <- Predictor$new(learner, data = Boston)
 mod.S3 <- mod.mlr$learner.model
 predictor.S3 <- Predictor$new(mod.S3, data = Boston)
 
+skip_if_not_installed("caret")
 # caret
 mod.caret <- caret::train(medv ~ .,
   data = Boston, method = "knn",
@@ -266,6 +270,7 @@ test_that("Predictor errors with data, which includes NAs.", {
 })
 
 test_that("Predictor keeps factor names without X", {
+  skip_if_not_installed("rpart")
   require(rpart)
   dat <- data.frame(
     y = factor(rep(c(1, 2), times = 5)),

diff --git a/tests/testthat/test-create_predict_fun.R b/tests/testthat/test-create_predict_fun.R
@@ -1,3 +1,7 @@
+skip_if_not_installed("mlr")
+skip_if_not_installed("mlr3")
+skip_if_not_installed("caret")
+
 library("mlr")
 library("mlr3")
 library("caret")

diff --git a/tests/testthat/test-find_y.R b/tests/testthat/test-find_y.R
@@ -1,4 +1,5 @@
 test_that("find_y works with mlr", {
+  skip_if_not_installed("mlr")
   require("mlr")
   data(cars, package = "datasets")
 
@@ -10,6 +11,7 @@ test_that("find_y works with mlr", {
 })
 
 test_that("find_y works with caret", {
+  skip_if_not_installed("caret")
   require("caret")
   data(cars, package = "caret")
 
@@ -30,6 +32,7 @@ test_that("find_y works with lm", {
 
 
 test_that("find_y works with rpart", {
+  skip_if_not_installed("rpart")
   require("rpart")
   data(cars, package = "datasets")
 

diff --git a/tests/testthat/test-inferTask.R b/tests/testthat/test-inferTask.R
@@ -1,9 +1,11 @@
+skip_if_not_installed("randomForest")
 library("randomForest")
 
 rf <- randomForest(Species ~ ., data = iris, ntree = 1)
 f <- function(x) predict(rf, newdata = x)
 
 test_that("classificaton", {
+  skip_if_not_installed("mlr")
   ## use linear discriminant analysis to classify iris data
   task <- mlr::makeClassifTask(data = iris, target = "Species")
   learner <- mlr::makeLearner("classif.lda", method = "mle")
@@ -13,6 +15,7 @@ test_that("classificaton", {
   })
   expect_equal(tsk, "classification")
 
+  skip_if_not_installed("mlr3")
   task <- mlr3::TaskClassif$new(id = "iris", backend = iris, target = "Species")
   learner <- mlr3::lrn("classif.rpart")
   mod <- learner$train(task)
@@ -21,7 +24,8 @@ test_that("classificaton", {
 
   TrainData <- iris[, 1:4]
   TrainClasses <- iris[, 5]
-
+
+  skip_if_not_installed("caret")
   knnFit1 <- caret::train(TrainData, TrainClasses,
     method = "knn",
     preProcess = c("center", "scale"),
@@ -39,14 +43,14 @@ test_that("classificaton", {
 })
 
 test_that("regression", {
-
+  skip_if_not_installed("mlr")
   ## use linear discriminant analysis to classify iris data
   task <- mlr::makeRegrTask(data = cars, target = "dist")
   learner <- mlr::makeLearner("regr.lm")
   mod <- mlr::train(learner, task)
   expect_equal(inferTaskFromModel(mod), "regression")
 
-
+  skip_if_not_installed("caret")
   lmFit <- caret::train(dist ~ .,
     data = cars,
     method = "lm"

diff --git a/tic.R b/tic.R