Removed the need to use tidytext::reorder_within

R/nn2poly_methods.R

@@ -168,6 +168,32 @@ predict.nn2poly <- function(object, newdata, layers = NULL, ...) {
 #' `x` is generated using `nn2poly()` with `keep_layers=TRUE`.
 #'
 #' @examples
+#' # --- Single polynomial output ---
+#' # Build a NN structure with random weights, with 2 (+ bias) inputs,
+#' # 4 (+bias) neurons in the first hidden layer with "tanh" activation
+#' # function, 4 (+bias) neurons in the second hidden layer with "softplus",
+#' # and 2 "linear" output units
+#'
+#' weights_layer_1 <- matrix(rnorm(12), nrow = 3, ncol = 4)
+#' weights_layer_2 <- matrix(rnorm(20), nrow = 5, ncol = 4)
+#' weights_layer_3 <- matrix(rnorm(5), nrow = 5, ncol = 1)
+#'
+#' # Set it as a list with activation functions as names
+#' nn_object = list("tanh" = weights_layer_1,
+#'                  "softplus" = weights_layer_2,
+#'                  "linear" = weights_layer_3)
+#'
+#' # Obtain the polynomial representation (order = 3) of that neural network
+#' final_poly <- nn2poly(nn_object, max_order = 3)
+#'
+#' # Plot all the coefficients, one plot per output unit
+#' plot(final_poly)
+#'
+#' # Plot only the 5 most important coeffcients (by absolute magnitude)
+#' # one plot per output unit
+#' plot(final_poly, n = 5)
+#'
+#' # --- Multiple output polynomials ---
 #' # Build a NN structure with random weights, with 2 (+ bias) inputs,
 #' # 4 (+bias) neurons in the first hidden layer with "tanh" activation
 #' # function, 4 (+bias) neurons in the second hidden layer with "softplus",
@@ -277,11 +303,13 @@ plot.nn2poly <- function(x, ..., n=NULL) {
     scale_labels <- c("-")
   }
 
+  # inspired by tidytext::reorder_within
+  new_x <- do.call(paste, c(list(all_df$name, sep = "___"), list(all_df$type)))
+  reorder_aux <- stats::reorder(new_x, all_df$value, FUN = mean, decreasing = TRUE)
+
 
   plot_all <- ggplot2::ggplot(all_df,
-                              ggplot2::aes(x = tidytext::reorder_within(x = .data$name,
-                                                                        by = -.data$value,
-                                                                        within = .data$type),
+                              ggplot2::aes(x = reorder_aux,
                                            y = .data$value,
                                            fill = .data$sign)) +
     ggplot2::geom_bar(stat = "identity", colour = "black", alpha = 1) +