Fix R devel notes (#389)

* Enabling the MSEv criterion in the explain function for python wrapper

* Update the examples to upack the `MSEv` criterion from the call to `explain()`.

* Added `MSEv_uniform_comb_weights` as a parameter

* Corrected the documentation.

* Add something and ...

* then remove it to make github check tests again.

* running all examples and adding MSEv to them

* fix R CMD check notes from R-devel

* fix names

* last_epoch -> last_state

* Update R/approach_vaeac_torch_modules.R

---------

Co-authored-by: LHBO <[email protected]>

R/approach_vaeac.R

@@ -2393,7 +2393,7 @@ vaeac_save_state <- function(state_list, file_name, return_state = FALSE) {
 #'
 #' @param best_epoch Positive integer. The epoch with the lowest validation error.
 #' @param best_epoch_running Positive integer. The epoch with the lowest running validation error.
-#' @param last_epoch The state list (i.e., the saved `vaeac` object)
+#' @param last_state The state list (i.e., the saved `vaeac` object)
 #' of `vaeac` model at the epoch with the lowest IWAE.
 #'
 #' @return This function only prints out a message.

R/approach_vaeac_torch_modules.R

@@ -815,7 +815,6 @@ vaeac_compute_normalization <- function(data, one_hot_max_sizes) {
 #' features are strictly positive, as for Burr and Abalone data, it can be advantageous to log-transform
 #' the data to unbounded form before using vaeac. If TRUE, then `vaeac_postprocess_data` will
 #' take the exp of the results to get back to strictly positive values.
-#' @param x_torch Torch tensor. A 2D matrix containing the data to normalize.
 #' @param norm_mean Torch tensor (optional). A 1D array containing the means of the columns of `x_torch`.
 #' @param norm_std Torch tensor (optional). A 1D array containing the stds of the columns of `x_torch`.
 #'
@@ -1995,9 +1994,6 @@ specified_prob_mask_generator <- torch::nn_module(
 #' If TRUE, then batch must be sampled using 'paired_sampler' which creates batches where
 #' the first half and second half of the rows are duplicates of each other. That is,
 #' `batch = [row1, row1, row2, row2, row3, row3, ...]`.
-#' @param batch Matrix/Tensor. Only used to get the dimensions and to check if any of the
-#' entries are missing. If any are missing, then the returned mask will ensure that
-#' these missing entries are masked.
 #'
 #' @examples
 #' \dontrun{

R/documentation.R

@@ -23,7 +23,7 @@
 #'
 #'
 #' @keywords internal
-default_doc <- function() {
+default_doc <- function(internal, model, predict_model, output_size, extra, ...) {
   NULL
 }
 
@@ -36,6 +36,6 @@ default_doc <- function() {
 #' apply to the present method. `NULL` means all combinations. Only used internally.
 #'
 #' @keywords internal
-default_doc_explain <- function() {
+default_doc_explain <- function(internal, index_features) {
   NULL
 }

R/finalize_explanation.R

@@ -111,8 +111,10 @@ get_p <- function(dt_vS, internal) {
 }
 
 #' Compute shapley values
-#' @param explainer An `explain` object.
 #' @param dt_vS The contribution matrix.
+#'
+#' @inheritParams default_doc
+#'
 #' @return A `data.table` with Shapley values for each test observation.
 #' @export
 #' @keywords internal