Add french FR translation (#131)

* switch to base:: messaging system for the translation and add FR translation

DESCRIPTION

@@ -22,7 +22,6 @@ Imports:
     torch (>= 0.4.0),
     hardhat (>= 1.3.0),
     magrittr,
-    glue,
     progress,
     rlang,
     methods,

NEWS.md

@@ -1,6 +1,7 @@
 # tabnet (development version)
 
 ## New features
+* add FR translation (#131)
 * `tabnet_pretrain()` now allows different GLU blocks in GLU layers in encoder and in decoder through the `config()` parameters `num_idependant_decoder` and `num_shared_decoder` (#129) 
 * {tabnet} now allows hierarchical multi-label classification through {data.tree} hierarchical `Node` dataset.  (#126) 
 * Add `reduce_on_plateau` as option for `lr_scheduler` at `tabnet_config()` (@SvenVw, #120)

R/dials.R

@@ -1,6 +1,6 @@
 check_dials <- function() {
   if (!requireNamespace("dials", quietly = TRUE))
-    rlang::abort("Package \"dials\" needed for this function to work. Please install it.")
+    stop("Package \"dials\" needed for this function to work. Please install it.", call. = FALSE)
 }
 
 

R/explain.R

@@ -44,10 +44,9 @@ tabnet_explain <- function(object, new_data) {
 #' @export
 #' @rdname tabnet_explain
 tabnet_explain.default <- function(object, new_data) {
-  stop(
-    "`tabnet_explain()` is not defined for a '", class(object)[1], "'.",
-    call. = FALSE
-  )
+  stop(domain=NA,
+       gettextf("`tabnet_explain()` is not defined for a '%s'.", class(object)[1]),
+       call. = FALSE)
 }
 
 #' @export

R/hardhat.R

@@ -104,10 +104,9 @@ tabnet_fit <- function(x, ...) {
 #' @export
 #' @rdname tabnet_fit
 tabnet_fit.default <- function(x, ...) {
-  stop(
-    "`tabnet_fit()` is not defined for a '", class(x)[1], "'.",
-    call. = FALSE
-  )
+  stop(domain=NA,
+       gettextf("`tabnet_fit()` is not defined for a '%s'.", class(x)[1]),
+       call. = FALSE)
 }
 
 #' @export
@@ -293,10 +292,9 @@ tabnet_pretrain <- function(x, ...) {
 #' @export
 #' @rdname tabnet_pretrain
 tabnet_pretrain.default <- function(x, ...) {
-  stop(
-    "`tabnet_pretrain()` is not defined for a '", class(x)[1], "'.",
-    call. = FALSE
-  )
+  stop(domain=NA,
+       gettextf("`tabnet_pretrain()` is not defined for a '%s'.", class(x)[1]),
+       call. = FALSE)
 }
 
 
@@ -390,13 +388,14 @@ tabnet_bridge <- function(processed, config = tabnet_config(), tabnet_model, fro
   epoch_shift <- 0L
 
   if (!(is.null(tabnet_model) || inherits(tabnet_model, "tabnet_fit") || inherits(tabnet_model, "tabnet_pretrain")))
-    rlang::abort(glue::glue("{tabnet_model} is not recognised as a proper TabNet model"))
+    stop(gettextf("'%s' is not recognised as a proper TabNet model", tabnet_model),
+         call. = FALSE)
 
   if (!is.null(from_epoch) && !is.null(tabnet_model)) {
     # model must be loaded from checkpoint
 
     if (from_epoch > (length(tabnet_model$fit$checkpoints) * tabnet_model$fit$config$checkpoint_epoch))
-      rlang::abort(glue::glue("The model was trained for less than {from_epoch} epochs"))
+      stop(gettextf("The model was trained for less than '%s' epochs", from_epoch), call. = FALSE)
 
     # find closest checkpoint for that epoch
     closest_checkpoint <- from_epoch %/% tabnet_model$fit$config$checkpoint_epoch
@@ -408,7 +407,7 @@ tabnet_bridge <- function(processed, config = tabnet_config(), tabnet_model, fro
   }
   if (task == "supervised") {
     if (sum(is.na(outcomes)) > 0) {
-      rlang::abort(glue::glue("Error: found missing values in the `{names(outcomes)}` outcome column."))
+      stop(gettextf("Found missing values in the `%s` outcome column.", names(outcomes)), call. = FALSE)
     }
     if (is.null(tabnet_model)) {
       # new supervised model needs network initialization
@@ -418,7 +417,7 @@ tabnet_bridge <- function(processed, config = tabnet_config(), tabnet_model, fro
     } else if (!check_net_is_empty_ptr(tabnet_model) && inherits(tabnet_model, "tabnet_fit")) {
       # resume training from supervised
       if (!identical(processed$blueprint, tabnet_model$blueprint))
-        rlang::abort("Model dimensions don't match.")
+        stop("Model dimensions don't match.", call. = FALSE)
 
       # model is available from tabnet_model$serialized_net
       m <- reload_model(tabnet_model$serialized_net)
@@ -443,7 +442,7 @@ tabnet_bridge <- function(processed, config = tabnet_config(), tabnet_model, fro
       tabnet_model$fit$network <- reload_model(tabnet_model$fit$checkpoints[[last_checkpoint]])
       epoch_shift <- last_checkpoint * tabnet_model$fit$config$checkpoint_epoch
 
-    } else rlang::abort(glue::glue("No model serialized weight can be found in {tabnet_model} check the model history"))
+    } else stop(gettextf("No model serialized weight can be found in `%s`, check the model history", tabnet_model), call. = FALSE)
 
     fit_lst <- tabnet_train_supervised(tabnet_model, predictors, outcomes, config = config, epoch_shift)
     return(new_tabnet_fit(fit_lst, blueprint = processed$blueprint))
@@ -485,7 +484,7 @@ predict_tabnet_bridge <- function(type, object, predictors, epoch, batch_size) {
   if (!is.null(epoch)) {
 
     if (epoch > (length(object$fit$checkpoints) * object$fit$config$checkpoint_epoch))
-      rlang::abort(glue::glue("The model was trained for less than {epoch} epochs"))
+      stop(gettextf("The model was trained for less than `%s` epochs", epoch), call. = FALSE)
 
     # find closest checkpoint for that epoch
     ind <- epoch %/% object$fit$config$checkpoint_epoch
@@ -530,7 +529,7 @@ model_pretrain_to_fit <- function(obj, x, y, config = tabnet_config()) {
   m <- reload_model(obj$serialized_net)
 
   if (m$input_dim != tabnet_model_lst$network$input_dim)
-    rlang::abort("Model dimensions don't match.")
+    stop("Model dimensions don't match.", call. = FALSE)
 
   # perform update of selected weights into new tabnet_model
   m_stat_dict <- m$state_dict()
@@ -582,25 +581,25 @@ check_type <- function(outcome_ptype, type = NULL) {
   outcome_all_numeric <- all(purrr::map_lgl(outcome_ptype, is.numeric))
 
   if (!outcome_all_numeric && !outcome_all_factor)
-    rlang::abort(glue::glue("Mixed multi-outcome type '{unique(purrr::map_chr(outcome_ptype, ~class(.x)[[1]]))}' is not supported"))
+    stop(gettextf("Mixed multi-outcome type '%s' is not supported", unique(purrr::map_chr(outcome_ptype, ~class(.x)[[1]]))), call. = FALSE)
 
   if (is.null(type)) {
     if (outcome_all_factor)
       type <- "class"
     else if (outcome_all_numeric)
       type <- "numeric"
     else if (ncol(outcome_ptype) == 1)
-      rlang::abort(glue::glue("Unknown outcome type '{class(outcome_ptype)}'"))
+      stop(gettextf("Unknown outcome type '%s'", class(outcome_ptype)), call. = FALSE)
   }
 
   type <- rlang::arg_match(type, c("numeric", "prob", "class"))
 
   if (outcome_all_factor) {
     if (!type %in% c("prob", "class"))
-      rlang::abort(glue::glue("Outcome is factor and the prediction type is '{type}'."))
+      stop(gettextf("Outcome is factor and the prediction type is '%s'.", type), call. = FALSE)
   } else if (outcome_all_numeric) {
     if (type != "numeric")
-      rlang::abort(glue::glue("Outcome is numeric and the prediction type is '{type}'."))
+      stop(gettextf("Outcome is numeric and the prediction type is '%s'.", type), call. = FALSE)
   }
 
   invisible(type)
@@ -638,15 +637,15 @@ check_compliant_node <- function(node) {
     reserved_names <- c(paste0("level_", c(1:node_height)), data.tree::NODE_RESERVED_NAMES_CONST)
     actual_names <- colnames(node)[!colnames(node) %in% "pathString"]
   } else {
-    rlang::abort("The provided hierarchical object is not recognized with a valid format that can be checked")
+    stop("The provided hierarchical object is not recognized with a valid format that can be checked", call. = FALSE)
   }
 
   if (any(actual_names %in% reserved_names)) {
-    rlang::abort(paste0(
-      "The attributes or colnames in the provided hierarchical object use the following reserved names : '",
-      paste(actual_names[actual_names %in% reserved_names], collapse = "', '"),
-      "'. Please change those names as they will lead to unexpected tabnet behavior."
-      ))
+    stop(domain=NA,
+         gettextf("The attributes or colnames in the provided hierarchical object use the following reserved names : '%s'. Please change those names as they will lead to unexpected tabnet behavior.",
+          paste(actual_names[actual_names %in% reserved_names], collapse = "', '")
+         ),
+         call. = FALSE)
   }
 
   invisible(node)

R/model.R

@@ -239,7 +239,7 @@ resolve_loss <- function(config, dtype) {
     # cross entropy loss is required
     loss_fn <- torch::nn_cross_entropy_loss()
   else
-    rlang::abort(glue::glue("{loss} is not a valid loss for outcome of type {dtype}"))
+    stop(gettextf("`%s` is not a valid loss for outcome of type %s", loss, dtype), call. = FALSE)
 
   loss_fn
 }
@@ -250,7 +250,7 @@ resolve_early_stop_monitor <- function(early_stopping_monitor, valid_split) {
   else if (early_stopping_monitor %in% c("train_loss", "auto"))
     early_stopping_monitor <- "train_loss"
   else
-    rlang::abort(glue::glue("{early_stopping_monitor} is not a valid early-stopping metric to monitor with `valid_split` = {valid_split}"))
+    stop(gettextf("%s is not a valid early-stopping metric to monitor with `valid_split` = %s", early_stopping_monitor, valid_split), call. = FALSE)
 
   early_stopping_monitor
 }
@@ -506,7 +506,7 @@ tabnet_train_supervised <- function(obj, x, y, config = tabnet_config(), epoch_s
     if (config$optimizer == "adam")
       optimizer <- torch::optim_adam(network$parameters, lr = config$learn_rate)
     else
-      rlang::abort("Currently only the 'adam' optimizer is supported.")
+      stop("Currently only the 'adam' optimizer is supported.", call. = FALSE)
 
   }
 
@@ -520,7 +520,7 @@ tabnet_train_supervised <- function(obj, x, y, config = tabnet_config(), epoch_s
   } else if (config$lr_scheduler == "step") {
     scheduler <- torch::lr_step(optimizer, config$step_size, config$lr_decay)
   } else {
-    rlang::abort("Currently only the 'step' and 'reduce_on_plateau' scheduler are supported.")
+    stop("Currently only the 'step' and 'reduce_on_plateau' scheduler are supported.", call. = FALSE)
   }
 
   # restore previous metrics & checkpoints
@@ -565,12 +565,11 @@ tabnet_train_supervised <- function(obj, x, y, config = tabnet_config(), epoch_s
       metrics[[epoch]][["valid"]] <- transpose_metrics(valid_metrics)
     }
 
-    message <- sprintf("[Epoch %03d] Loss: %3f", epoch, mean(metrics[[epoch]]$train$loss))
-    if (has_valid)
-      message <- paste0(message, sprintf(" Valid loss: %3f", mean(metrics[[epoch]]$valid$loss)))
+    if (config$verbose & !has_valid)
+      message(gettextf("[Epoch %03d] Loss: %3f", epoch, mean(metrics[[epoch]]$train$loss)))
+    if (config$verbose & has_valid)
+      message(gettextf("[Epoch %03d] Loss: %3f, Valid loss: %3f", epoch, mean(metrics[[epoch]]$train$loss), mean(metrics[[epoch]]$valid$loss)))
 
-    if (config$verbose)
-      rlang::inform(message)
 
     # Early-stopping checks
     if (config$early_stopping && config$early_stopping_monitor=="valid_loss"){
@@ -585,7 +584,7 @@ tabnet_train_supervised <- function(obj, x, y, config = tabnet_config(), epoch_s
         patience_counter <- patience_counter + 1
         if (patience_counter >= config$early_stopping_patience){
           if (config$verbose)
-            rlang::inform(sprintf("Early stopping at epoch %03d", epoch))
+            message(gettextf("Early stopping at epoch %03d", epoch))
           break
         }
       } else {
@@ -610,9 +609,10 @@ tabnet_train_supervised <- function(obj, x, y, config = tabnet_config(), epoch_s
   if(!config$skip_importance) {
     importance_sample_size <- config$importance_sample_size
     if (is.null(config$importance_sample_size) && train_ds$.length() > 1e5) {
-      rlang::warn(c(glue::glue("Computing importances for a dataset with size {train_ds$.length()}."),
-                    "This can consume too much memory. We are going to use a sample of size 1e5",
-                    "You can disable this message by using the `importance_sample_size` argument."))
+      warning(
+        gettextf(
+          "Computing importances for a dataset with size %s. This can consume too much memory. We are going to use a sample of size 1e5, You can disable this message by using the `importance_sample_size` argument.",
+          train_ds$.length()))
       importance_sample_size <- 1e5
     }
     indexes <- as.numeric(torch::torch_randint(

R/parsnip.R

@@ -242,7 +242,7 @@ tabnet <- function(mode = "unknown", epochs = NULL, penalty = NULL, batch_size =
                    num_independent = NULL, num_shared = NULL, momentum = NULL) {
 
   if (!requireNamespace("parsnip", quietly = TRUE))
-    rlang::abort("Package \"parsnip\" needed for this function to work. Please install it.")
+    stop("Package \"parsnip\" needed for this function to work. Please install it.", call. = FALSE)
 
   if (!tabnet_env$parsnip_added) {
     add_parsnip_tabnet()

R/pretraining.R

@@ -136,7 +136,7 @@ tabnet_train_unsupervised <- function(x, config = tabnet_config(), epoch_shift =
     if (config$optimizer == "adam")
       optimizer <- torch::optim_adam(network$parameters, lr = config$learn_rate)
     else
-      rlang::abort("Currently only the 'adam' optimizer is supported.")
+      stop("Currently only the 'adam' optimizer is supported.", call. = FALSE)
 
   }
 
@@ -150,7 +150,7 @@ tabnet_train_unsupervised <- function(x, config = tabnet_config(), epoch_shift =
   } else if (config$lr_scheduler == "step") {
     scheduler <- torch::lr_step(optimizer, config$step_size, config$lr_decay)
   } else {
-    rlang::abort("Currently only the 'step' and 'reduce_on_plateau' scheduler are supported.")
+    stop("Currently only the 'step' and 'reduce_on_plateau' scheduler are supported.", call. = FALSE)
   }
 
   # initialize metrics & checkpoints
@@ -195,12 +195,10 @@ tabnet_train_unsupervised <- function(x, config = tabnet_config(), epoch_shift =
       metrics[[epoch]][["valid"]] <- transpose_metrics(valid_metrics)
     }
 
-    message <- sprintf("[Epoch %03d] Loss: %3f", epoch, mean(metrics[[epoch]]$train$loss))
-    if (has_valid)
-      message <- paste0(message, sprintf(" Valid loss: %3f", mean(metrics[[epoch]]$valid$loss)))
-
-    if (config$verbose)
-      rlang::inform(message)
+    if (config$verbose & !has_valid)
+      message(gettextf("[Epoch %03d] Loss: %3f", epoch, mean(metrics[[epoch]]$train$loss)))
+    if (config$verbose & has_valid)
+      message(gettextf("[Epoch %03d] Loss: %3f, Valid loss: %3f", epoch, mean(metrics[[epoch]]$train$loss), mean(metrics[[epoch]]$valid$loss)))
 
     # Early-stopping checks
     if (config$early_stopping && config$early_stopping_monitor=="valid_loss"){
@@ -240,9 +238,9 @@ tabnet_train_unsupervised <- function(x, config = tabnet_config(), epoch_shift =
 
   importance_sample_size <- config$importance_sample_size
   if (is.null(config$importance_sample_size) && train_ds$.length() > 1e5) {
-    rlang::warn(c(glue::glue("Computing importances for a dataset with size {train_ds$.length()}."),
-                  "This can consume too much memory. We are going to use a sample of size 1e5",
-                  "You can disable this message by using the `importance_sample_size` argument."))
+    warning(domain=NA,
+            gettextf("Computing importances for a dataset with size %s. This can consume too much memory. We are going to use a sample of size 1e5. You can disable this message by using the `importance_sample_size` argument.", train_ds$.length()),
+            call. = FALSE)
     importance_sample_size <- 1e5
   }
   indexes <- as.numeric(torch::torch_randint(

R/tab-network.R

@@ -266,9 +266,9 @@ tabnet_pretrainer <- torch::nn_module(
     self$initial_bn <- torch::nn_batch_norm1d(self$input_dim, momentum = momentum)
 
     if (self$n_steps <= 0)
-      stop("n_steps should be a positive integer.")
+      stop("'n_steps' should be a positive integer.")
     if (self$n_independent == 0 && self$n_shared == 0)
-      stop("n_shared and n_independant can't be both zero.")
+      stop("'n_shared' and 'n_independant' can't be both zero.")
 
     self$virtual_batch_size <- virtual_batch_size
     self$embedder <- embedding_generator(input_dim, cat_dims, cat_idxs, cat_emb_dim)
@@ -452,9 +452,9 @@ tabnet_nn <- torch::nn_module(
     self$mask_type <- mask_type
 
     if (self$n_steps <= 0)
-      stop("n_steps should be a positive integer.")
+      stop("'n_steps' should be a positive integer.")
     if (self$n_independent == 0 && self$n_shared == 0)
-      stop("n_shared and n_independant can't be both zero.")
+      stop("'n_shared' and 'n_independant' can't be both zero.")
 
     self$virtual_batch_size <- virtual_batch_size
     self$embedder <- embedding_generator(input_dim, cat_dims, cat_idxs, cat_emb_dim)
@@ -494,7 +494,7 @@ attentive_transformer <- torch::nn_module(
     else if (mask_type == "entmax")
       self$selector <- entmax(dim = -1)
     else
-      stop("Please choose either sparsemax or entmax as masktype")
+      stop("Please choose either 'sparsemax' or 'entmax' as 'mask_type'")
 
   },
   forward = function(priors, processed_feat) {