merge with last changes in master

Merge branch 'master' into docs_revision

# Conflicts:
#	R/obtain_derivatives_list.R

.github/workflows/build.yml

@@ -5,13 +5,14 @@ jobs:
   build:
     if: ${{ !contains(github.event.head_commit.message, '[ci skip]') }}
     runs-on: ${{ matrix.config.os }}
-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+    name: ${{ matrix.config.os }} ${{ matrix.config.arch }} (${{ matrix.config.r }})
 
     strategy:
       fail-fast: false
       matrix:
         config:
-        - {os: macOS-latest, tf: 'default', r: 'release'}
+        - {os: macOS-latest, tf: 'default', r: 'release', arch: 'x86'}
+        - {os: macOS-latest, tf: 'default', r: 'release', arch: 'arm64'}
         - {os: windows-latest, tf: 'default', r: 'release'}
         - {os: ubuntu-latest, tf: 'default', r: 'devel', http-user-agent: 'release'}
         - {os: ubuntu-latest, tf: 'default', r: 'release', covr: 'yes'}
@@ -34,10 +35,11 @@ jobs:
       with:
         extra-packages: any::rcmdcheck, any::covr, [email protected]
         needs: check, coverage
-    - name: Miniconda + Tensorflow + Keras installation
+    - name: Miniconda + Tensorflow + Keras + Torch installation
       run: |
         reticulate::install_miniconda()
         keras::install_keras(tensorflow = '${{ matrix.tf }}-cpu')
+        torch::install_torch()
       shell: Rscript {0}
 
     - name: Check

DESCRIPTION

@@ -19,6 +19,7 @@ Encoding: UTF-8
 LazyData: true
 Depends: R (>= 3.5.0)
 Imports:
+    generics,
     matrixStats,
     pracma,
     Rcpp
@@ -27,12 +28,14 @@ Suggests:
     ggplot2,
     keras,
     knitr,
+    luz,
     patchwork,
     rmarkdown,
     R6,
     tensorflow,
     testthat (>= 3.0.0),
     tidytext,
+    torch,
     vdiffr
 LinkingTo:
     Rcpp,

NAMESPACE

@@ -1,18 +1,23 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(add_constraints,keras.engine.training.Model)
+S3method(add_constraints,luz_module_generator)
+S3method(fit,nn2poly)
 S3method(nn2poly,default)
-S3method(nn2poly,keras.engine.training.Model)
+S3method(nn2poly,list)
 S3method(plot_taylor_and_activation_potentials,default)
-S3method(plot_taylor_and_activation_potentials,keras.engine.training.Model)
+S3method(plot_taylor_and_activation_potentials,list)
 S3method(predict,nn2poly)
 export(add_constraints)
-export(change_string_to_function)
 export(eval_poly)
+export(fit)
+export(luz_model_sequential)
 export(nn2poly)
 export(obtain_derivatives_list)
 export(plot_diagonal)
 export(plot_n_important_coeffs)
 export(plot_taylor_and_activation_potentials)
 importFrom(Rcpp,sourceCpp)
+importFrom(generics,fit)
 importFrom(stats,density)
 useDynLib(nn2poly, .registration = TRUE)

R/RcppExports.R

@@ -1,23 +1,23 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-select_allowed_partitions <- function(equivalent_label, q_previous_layer, labels, partitions) {
-    .Call(`_nn2poly_select_allowed_partitions`, equivalent_label, q_previous_layer, labels, partitions)
-}
-
-alg_non_linear <- function(coeffs_input, labels_input, labels_output, q_taylor_vector, current_layer, g, partitions_labels, partitions) {
-    .Call(`_nn2poly_alg_non_linear`, coeffs_input, labels_input, labels_output, q_taylor_vector, current_layer, g, partitions_labels, partitions)
-}
-
-combinations_with_repetition <- function(n, k) {
-    .Call(`_nn2poly_combinations_with_repetition`, n, k)
-}
-
-generate_partitions <- function(p, q_max) {
-    .Call(`_nn2poly_generate_partitions`, p, q_max)
-}
-
-generate_partitions_full <- function(p, q_max) {
-    .Call(`_nn2poly_generate_partitions_full`, p, q_max)
-}
-
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+select_allowed_partitions <- function(equivalent_label, q_previous_layer, labels, partitions) {
+    .Call(`_nn2poly_select_allowed_partitions`, equivalent_label, q_previous_layer, labels, partitions)
+}
+
+alg_non_linear <- function(coeffs_input, labels_input, labels_output, q_taylor_vector, current_layer, g, partitions_labels, partitions) {
+    .Call(`_nn2poly_alg_non_linear`, coeffs_input, labels_input, labels_output, q_taylor_vector, current_layer, g, partitions_labels, partitions)
+}
+
+combinations_with_repetition <- function(n, k) {
+    .Call(`_nn2poly_combinations_with_repetition`, n, k)
+}
+
+generate_partitions <- function(p, q_max) {
+    .Call(`_nn2poly_generate_partitions`, p, q_max)
+}
+
+generate_partitions_full <- function(p, q_max) {
+    .Call(`_nn2poly_generate_partitions_full`, p, q_max)
+}
+

R/add_constraints.R

@@ -8,9 +8,6 @@
 # but L1 is preferred, with 1 as it maximum norm bound.
 
 
-# Set Python vars to NULL to avoid global variable notes in package check
-super <- self <- constraint_maxnorm <- NULL
-
 
 #' Custom keras L1 constraint.
 #'
@@ -25,6 +22,7 @@ super <- self <- constraint_maxnorm <- NULL
 #' the custom layers implemented here, where bias and weights will be joined
 #' together.
 #'
+#' @noRd
 constraint_l1_norm <- function(w) {
   norms   = keras::k_sum(keras::k_abs(w), axis = 1, keepdims = TRUE)
   desired = keras::k_clip(norms, 0, 1)
@@ -42,19 +40,29 @@ constraint_l1_norm <- function(w) {
 #' layers that impose L1 and L2 norm constraints
 #'
 #' @param model A keras model to which the restrictions on the weights are to be applied.
-#' @param constraint_type The type of constraint you want to apply on the model. Currently,
+#' @param type The type of constraint you want to apply on the model. Currently,
 #' 'l1_norm' and 'l2_norm' can be applied.
 #' @param keep_old_weights Binary parameters that controls if the weights of
 #' \code{model} are kept in the new constrained network.
+#' @param ... Additional arguments (unused).
 #'
 #' @return A keras model with the custom constraints applied.
+#'
 #' @export
 add_constraints <- function(model,
-                            constraint_type  = "l1_norm",
-                            keep_old_weights = FALSE) {
-  if (!requireNamespace("keras", quietly = TRUE)) {
+                            type = c("l1_norm", "l2_norm"),
+                            keep_old_weights = FALSE,
+                            ...) {
+  UseMethod("add_constraints")
+}
+
+#' @export
+add_constraints.keras.engine.training.Model <- function(model,
+                                                        type = c("l1_norm", "l2_norm"),
+                                                        keep_old_weights = FALSE,
+                                                        ...) {
+  if (!requireNamespace("keras", quietly = TRUE))
     stop("package 'keras' is required for this functionality", call.=FALSE)
-  }
 
   # Custom keras layers using L1 and L2 norms. These are needed to impose
   # constraints simultaneously to the weights and bias.
@@ -100,7 +108,7 @@ add_constraints <- function(model,
           initializer = "random_normal",
           trainable = TRUE,
           # maxnorm uses the L2 norm with a given max value
-          constraint = constraint_maxnorm(max_value = 1, axis = 0)
+          constraint = keras::constraint_maxnorm(max_value = 1, axis = 0)
         )
       },
       call = function(inputs) {
@@ -115,12 +123,12 @@ add_constraints <- function(model,
   layer_combined_L1 <- keras::create_layer_wrapper(Layer_Combined_L1)
   layer_combined_L2 <- keras::create_layer_wrapper(Layer_Combined_L2)
 
-  params      <- get_model_parameters(model)
+  params      <- get_parameters(model)
   nlayers     <- length(params$weights_list)
   new_layers  <- vector(mode = "list", length = nlayers) # create a list of new layers
 
   # choose the custom layer to use
-  custom_layer <- switch(constraint_type,
+  custom_layer <- switch(match.arg(type),
                          "l1_norm" = layer_combined_L1,
                          "l2_norm" = layer_combined_L2,
                          layer_combined_L1)
@@ -161,3 +169,12 @@ add_constraints <- function(model,
   new_model
 }
 
+#' @export
+add_constraints.luz_module_generator <- function(model,
+                                                 type = c("l1_norm", "l2_norm"),
+                                                 ...) {
+  luz_model_sequential_check(model)
+  attr(model, "constraint") <- match.arg(type)
+  class(model) <- c("nn2poly", class(model))
+  model
+}

R/callback.R

@@ -0,0 +1,28 @@
+#' Build the appropriate callback based on the model class
+#' @noRd
+build_callback <- function(object,
+                           type = c("l1_norm", "l2_norm")) {
+  UseMethod("build_callback")
+}
+
+norm_order <- function(type = c("l1_norm", "l2_norm")) {
+  as.numeric(substr(match.arg(type), 2, 2))
+}
+
+build_callback.luz_module_generator <- function(object,
+                                                type = c("l1_norm", "l2_norm")) {
+  luz_callback <- luz::luz_callback(
+    name = paste0(type, "_callback"),
+
+    initialize = function() {
+      self$constraint <- torch_constraint(norm_order(type))
+    },
+
+    on_train_batch_end = function() {
+      for (layer in utils::head(ctx$model$children, -1))
+        layer$apply(self$constraint)
+    }
+  )
+
+  luz_callback()
+}

R/change_string_to_function.R

@@ -5,8 +5,8 @@
 #' activation functions, i.e., "softplus", "tanh", "sigmoid" or "linear".
 #'
 #' @return List of R functions associated with the string names provided.
-#' @export
 #'
+#' @noRd
 change_string_to_function <- function(af_string_list) {
   n <- length(af_string_list)
 

R/check_weight_constraints.R

@@ -1,11 +1,11 @@
-
 #' Check weight's vector constraints (including bias)
 #'
 #' @param weights List of matrices with the weights of each layer.
 #' @param maxnorm List of 2 elements: the name of the used norm and its max value.
 #'
 #' @return List across all layers with a vector containing the norms of each weight vector.
 #'
+#' @noRd
 check_weight_constraints <- function(weights, maxnorm) {
   #### Compute the norm for the full matrix ####
 

R/check_weight_dimensions.R

@@ -4,6 +4,7 @@
 #'
 #' @return `TRUE` if the dimensiones are correct, `FALSE` if not.
 #'
+#' @noRd
 check_weights_dimensions <- function(weights_matrices_list) {
   for (matrix_index in 2:length(weights_matrices_list)) {
     nrows_current <- nrow(weights_matrices_list[[matrix_index]])

R/eval_poly.R

@@ -36,8 +36,6 @@
 #' eval_poly(x,poly)
 #'
 #' @export
-#'
-
 eval_poly <- function(x, poly) {
 
   # Remove names and transform into matrix (variables as columns)

R/fit.R

@@ -0,0 +1,9 @@
+#' @importFrom generics fit
+#' @export
+generics::fit
+
+#' @export
+fit.nn2poly <- function(object, ...) {
+  callback <- build_callback(object, attr(object, "constraint"))
+  NextMethod(callbacks = append(as.list(list(...)[["callbacks"]]), callback))
+}