docs: escape examples if torch not installed

R/DataDescriptor.R

@@ -40,6 +40,7 @@
 #' @export
 #' @include utils.R
 #' @seealso ModelDescriptor, lazy_tensor
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Create a dataset
 #' dsg = dataset(

R/LearnerTorchModel.R

@@ -26,6 +26,7 @@
 #' @family Graph Network
 #' @include LearnerTorch.R
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # We show the learner using a classification task
 #'

R/PipeOpModule.R

@@ -28,6 +28,7 @@
 #' @family Graph Network
 #' @family PipeOp
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' ## creating an PipeOpModule manually
 #'

R/PipeOpTaskPreprocTorch.R

@@ -88,6 +88,7 @@
 #'
 #' @include PipeOpTorch.R
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Creating a simple task
 #' d = data.table(

R/PipeOpTorch.R

@@ -82,6 +82,7 @@
 #' [`Graph`] which allows the graph to be modified by-reference in different parts of the code.
 #' @export
 #' @family Graph Network
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' ## Creating a neural network
 #' # In torch

R/PipeOpTorchCallbacks.R

@@ -20,6 +20,7 @@
 #' @family Model Configuration
 #' @family PipeOp
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' po_cb = po("torch_callbacks", "checkpoint")
 #' po_cb$param_set

R/PipeOpTorchIngress.R

@@ -123,6 +123,7 @@ PipeOpTorchIngress = R6Class("PipeOpTorchIngress",
 #' @return `TorchIngressToken` object.
 #' @family Graph Network
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Define a task for which we want to define an ingress token
 #' task = tsk("iris")
@@ -177,6 +178,7 @@ print.TorchIngressToken = function(x, ...) {
 #' @export
 #' @family Graph Network
 #' @family PipeOps
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 # We select the numeric features first
 #' graph = po("select", selector = selector_type(c("numeric", "integer"))) %>>%
@@ -227,6 +229,7 @@ register_po("torch_ingress_num", PipeOpTorchIngressNumeric)
 #' @family PipeOps
 #' @family Graph Network
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 # We first select the categorical features
 #' graph = po("select", selector = selector_type("factor")) %>>%
@@ -277,6 +280,7 @@ register_po("torch_ingress_categ", PipeOpTorchIngressCategorical)
 #' @family Graph Network
 #' @export
 #' @include utils.R shape.R
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' po_ingress = po("torch_ingress_ltnsr")
 #' task = tsk("lazy_iris")

R/PipeOpTorchLoss.R

@@ -17,6 +17,7 @@
 #' @family Model Configuration
 #'
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' po_loss = po("torch_loss", loss = t_loss("cross_entropy"))
 #' po_loss$param_set

R/PipeOpTorchModel.R

@@ -106,6 +106,7 @@ PipeOpTorchModel = R6Class("PipeOpTorchModel",
 #' @inheritSection mlr_pipeops_torch_model Internals
 #' @family PipeOps
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # simple logistic regression
 #'
@@ -150,6 +151,7 @@ PipeOpTorchModelClassif = R6Class("PipeOpTorchModelClassif",
 #' @inheritSection mlr_pipeops_torch_model Internals
 #' @family PipeOps
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # simple linear regression
 #'

R/PipeOpTorchOptimizer.R

@@ -21,6 +21,7 @@
 #' @family PipeOp
 #' @family Model Configuration
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' po_opt = po("torch_optimizer", "sgd", lr = 0.01)
 #' po_opt$param_set

R/TaskClassif_lazy_iris.R

@@ -21,6 +21,7 @@
 #'
 #' @references
 #' `r format_bib("anderson_1936")`
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' task = tsk("lazy_iris")
 #' df = task$data()

R/TorchCallback.R

@@ -149,6 +149,7 @@ as_torch_callbacks.character = function(x, clone = FALSE, ...) { # nolint
 #' @family Torch Descriptor
 #'
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Create a new torch callback from an existing callback set
 #' torch_callback = TorchCallback$new(CallbackSetCheckpoint)
@@ -247,6 +248,7 @@ TorchCallback = R6Class("TorchCallback",
 #' @return [`TorchCallback`]
 #' @include zzz.R CallbackSet.R
 #' @family Callback
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' custom_tcb = torch_callback("custom",
 #'   initialize = function(name) {

R/TorchLoss.R

@@ -55,6 +55,7 @@ as_torch_loss.character = function(x, clone = FALSE, ...) { # nolint
 #'
 #' @family Torch Descriptor
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Create a new torch loss
 #' torch_loss = TorchLoss$new(torch_loss = nn_mse_loss, task_types = "regr")

R/TorchOptimizer.R

@@ -57,6 +57,7 @@ as_torch_optimizer.character = function(x, clone = FALSE, ...) { # nolint
 #'
 #' @family Torch Descriptor
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Create a new torch loss
 #' torch_opt = TorchOptimizer$new(optim_adam, label = "adam")
@@ -146,6 +147,7 @@ TorchOptimizer = R6::R6Class("TorchOptimizer",
 #' @family Torch Descriptor
 #' @family Dictionary
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' mlr3torch_optimizers$get("adam")
 #' # is equivalent to

R/lazy_tensor.R

@@ -209,6 +209,7 @@ is_lazy_tensor = function(x) {
 #' 1. A new [`DataDescriptor`] is created
 #'
 #' @return [`lazy_tensor`]
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' lt = as_lazy_tensor(1:10)
 #' add_five = po("module", module = function(x) x + 5)

R/materialize.R

@@ -33,6 +33,7 @@
 #' @param ... (any)\cr
 #'   Additional arguments.
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' lt1 = as_lazy_tensor(torch_randn(10, 3))
 #' materialize(lt1, rbind = TRUE)

R/nn_graph.R

@@ -15,6 +15,7 @@
 #' @return [`nn_graph`]
 #' @family Graph Network
 #' @export
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' graph = mlr3pipelines::Graph$new()
 #' graph$add_pipeop(po("module_1", module = nn_linear(10, 20)), clone = FALSE)

R/reset_last_layer.R

@@ -17,10 +17,11 @@
 #' @return Same as input `model` with modified last layer.
 #' @export
 #'
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' \dontrun{
 #' # AlexNet
-#' if (mlr3misc::requires_namespaces("torchvision")) {
+#' if (mlr3misc::requires_namespaces("torchvision") && torch::torch_is_installed(h)) {
 #'  model = torchvision::model_alexnet(pretrained = TRUE)
 #'  model$classifier[[7]]$out_feature
 #'  model = reset_last_layer(model, 10)

R/task_dataset.R

@@ -20,6 +20,7 @@
 #'   The device, e.g. `"cuda"` or `"cpu"`.
 #' @export
 #' @return [`torch::dataset`]
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' task = tsk("iris")
 #' sepal_ingress = TorchIngressToken(

man-roxygen/learner_example.R

@@ -3,6 +3,7 @@
 #' <% id = paste0(task_types_vec[1], ".", name)%>
 #' <% task_id = default_task_id(lrn(id)) %>
 #'
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Define the Learner and set parameter values
 #' <%= sprintf("learner = lrn(\"%s\")", id)%>

man-roxygen/pipeop_torch_example.R

@@ -1,5 +1,6 @@
 #' <% param_vals = if (exists("param_vals", inherits = FALSE)) paste0(", ", param_vals)%>
 #'
+#' @examplesIf torch::torch_is_installed()
 #' @examples
 #' # Construct the PipeOp
 #' pipeop = po("<%=id %>"<%=param_vals%>)
@@ -10,6 +11,7 @@
 
 
 # Maybe finish this at some point
+# @examplesIf torch::torch_is_installed()
 # md = ModelDescriptor(
 #   graph =
 # )