update prepare CRAN

Author: i1es

NAMESPACE

@@ -22,8 +22,6 @@ export(create_model)
 export(create_vocabulary)
 export(evaluate)
 export(fit)
-export(get_task)
-export(get_vocabulary)
 export(max_case_length)
 export(num_outputs)
 export(predict)
@@ -35,6 +33,7 @@ export(vocab_size)
 import(bupaR)
 import(dplyr)
 import(eventdataR)
+import(ggplot2)
 import(keras)
 import(tensorflow)
 import(tidyr)

R/create_vocabulary.R

@@ -10,11 +10,6 @@
 #'
 #' @inheritParams tokenize
 #'
-#' @examples
-#' library(eventdataR)
-#' tasks <- c("outcome", "next_activity", "next_time", "remaining_time", "remaining_trace")
-#' purrr::map(tasks, ~prepare_examples_dt(patients, task = .x)) %>% purrr::map(create_vocabulary)
-#'
 #' @export
 create_vocabulary <- function(processed_df) {
 

R/prepare_examples_df.R

@@ -6,7 +6,7 @@
 #' @param log \code{\link{log}}: Object of class \code{\link{log}} or derivatives (\code{\link{grouped_log}}, \code{\link{eventlog}},
 #' \code{\link{activitylog}}, etc.).
 #' @param task [`character`]: a process monitoring task for which to prepare an event log.
-#' @param features [`character`] (default [`NULL`]): additional features. Appends attributes (if present) [numeric_features] and/or [categorical_features]  to a preprocessed event log.
+#' @param features [`character`] (default [`NULL`]): additional features. Appends attributes (if present) numeric_features and/or categorical_features to a preprocessed event log.
 #' @param ... additional arguments
 #'
 #' @return a preprocessed dataset

R/processpredictR.R

@@ -18,6 +18,7 @@
 #' @importFrom cli cli_abort
 #' @importFrom glue glue
 #' @importFrom plotly ggplotly
+#' @import ggplot2
 
 
 

R/stack_layers.R

@@ -2,7 +2,7 @@
 #'
 #' User friendly interface to add a keras layer on top of existing model.
 #'
-#' @param object [`ppred_model`]: a list containing a model
+#' @param object a list containing a model returned by create_model().
 #' @param ... functions for adding layers by using functional keras API. For example, keras::layer_dense(units=32, activation="relu")
 #'
 #' @export

R/tokenize.R

@@ -6,11 +6,6 @@
 #'
 #' @return A list of tokens: token_x and token_y
 #'
-#' @examples
-#' library(eventdataR)
-#' ex_remaining_trace <- prepare_examples(patients, task = "remaining_trace")
-#' tokenize(ex_remaining_trace, vocabulary = create_vocabulary(ex_remaining_trace))
-#'
 #' @export
 tokenize <- function(processed_df) {
   UseMethod("tokenize")

R/utils.R

@@ -3,15 +3,13 @@
 
 #' Utils
 #'
-#' @param examples [`ppred_examples_df`]: a preprocessed dataset returned by prepare_examples_dt().
+#' @param examples a preprocessed dataset returned by prepare_examples_dt().
 #'
-#' @export
 #'
 get_vocabulary <- function(examples) {
   attr(examples, "vocabulary")
 }
 
-#' @export
 get_task <- function(examples) {
   attr(examples, "task")
 }