Remove unnecessary files and pass check

.Rbuildignore

@@ -1,3 +1,6 @@
+^css$
+^js$
+^index\.html$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^CONVENTIONS\.md$

DESCRIPTION

@@ -19,6 +19,7 @@ Imports:
     graphics (>= 3.2.3),
     keras (>= 2.2.4),
     purrr (>= 0.2.4),
+    R.utils (>= 2.7.0),
     stats (>= 3.2.3),
     utils
 Suggests: 
@@ -30,6 +31,6 @@ VignetteBuilder: knitr
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 6.1.0
+RoxygenNote: 6.1.1
 SystemRequirements:
     Python (>= 2.7); keras <https://keras.io/> (>= 2.1)

NAMESPACE

@@ -90,5 +90,6 @@ export(save_as)
 export(sparsity)
 export(train)
 export(variational_block)
+import(R.utils)
 import(graphics)
 import(purrr)

R/00classes.R

@@ -27,5 +27,3 @@ ruta_loss_named <- "ruta_loss_named"
 ruta_loss_contraction <- "ruta_loss_contraction"
 ruta_loss_correntropy <- "ruta_loss_correntropy"
 ruta_loss_variational <- "ruta_loss_variational"
-
-ruta_arg <- "ruta_arg"

R/autoencoder.R

@@ -50,8 +50,7 @@ new_autoencoder <- function(network, loss, extra_class = NULL) {
 #' )
 #'
 #' @export
-autoencoder <- function(network = arg_network(), loss = arg_loss("mean_squared_error")) {
-  check_args(formals(), environment())
+autoencoder <- function(network, loss = "mean_squared_error") {
   new_autoencoder(network, loss)
 }
 
@@ -208,7 +207,7 @@ train.ruta_autoencoder <- function(
   data,
   validation_data = NULL,
   metrics = NULL,
-  epochs = arg_positive(20),
+  epochs = 20,
   optimizer = keras::optimizer_rmsprop(),
   ...) {
   learner$input_shape <- dim(data)[-1]

R/derivative.R

@@ -1,3 +1,5 @@
+# Functions in this file are no longer used, and only kept for backward compatibility.
+
 # This file defines the derivatives of activation functions available in Keras
 # (except softmax). They are not exported since this computation could change
 # in the future, in case Keras can automatically calculate derivatives for activations.

R/evaluate.R

@@ -28,6 +28,7 @@ evaluation_metric <- function(evaluate_f) function(learner, data, ...) {
 #'
 #' @param learner A trained learner object
 #' @param data Test data for evaluation
+#' @param ... Additional parameters passed to `keras::\link[keras]{evaluate}`.
 #' @return A named list with the autoencoder training loss and evaluation metric for the
 #'   given data
 #' @examples