library("reticulate")
library("mlr")
# helper function to skip tests if we don't have the conda
skip_because_conda_conf_needed <- function() {
if (!"CONDA_TEST" %in% names(Sys.getenv())) {
skip("Conda test env needed for tests")
}
}
skip_if_windows <- function() {
if (.Platform$OS.type == "windows")
skip("Test with unix")
}
skip_if_unix <- function() {
if (.Platform$OS.type == "unix")
skip("Test with windows")
}
# skip_if_no_mljar <- function() {
# if (!"MLJAR_TOKEN" %in% names(Sys.getenv())) {
# skip("MLJAR_TOKEN entry needed for tests")
# }
# }
skip_if_no_java <- function() {
if (!"JAVA" %in% names(Sys.getenv())) {
skip("JAVA entry needed for tests")
}
}
skip_if_osx <- function() {
# Needed becasue conda cannot remove pacakges while github actions osx build
if (Sys.info()["sysname"] == "Darwin") {
skip("Cannot test it with osx")
}
}
titanic_factorized <- titanic_imputed
titanic_factorized$survived <- as.factor(titanic_factorized$survived)
task_classification <- makeClassifTask(
id = "R",
data = titanic_factorized,
target = "survived"
)
task_multiclassification <- makeClassifTask(
id = "R",
data = HR,
target = "status"
)
learner_rf_classif <- makeLearner("classif.ranger", predict.type = "prob")
model_rf_classif <- train(learner_rf_classif, task_classification)
explainer_rf_classif <- explain_mlr(model_rf_classif, titanic_factorized, titanic_imputed$survived, label = "RF", verbose = FALSE)
learner_rpart_classif <- makeLearner("classif.rpart", predict.type = "prob")
model_rpart_classif <- train(learner_rpart_classif, task_classification)
explainer_rpart_classif <- explain_mlr(model_rpart_classif, titanic_factorized, titanic_imputed$survived, label = "RF", verbose = FALSE)
learner_rf_multiclassif <- makeLearner("classif.ranger", predict.type = "prob")
model_rf_multiclassif <- train(learner_rf_multiclassif, task_multiclassification)
explainer_rf_multiclassif <- explain_mlr(model_rf_multiclassif, HR, HR$status, label = "RF", verbose = FALSE)
learner_rpart_multiclassif <- makeLearner("classif.rpart", predict.type = "prob")
model_rpart_multiclassif <- train(learner_rpart_multiclassif, task_multiclassification)
explainer_rpart_multiclassif <- explain_mlr(model_rpart_multiclassif, HR, HR$status, label = "RF", verbose = FALSE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.