add decoder GLU layers specific number of blocks (#129)

* add decoder GRU layers specific number of parameters

NEWS.md

@@ -1,6 +1,7 @@
 # tabnet (development version)
 
 ## New features
+* `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/model.R

@@ -83,12 +83,16 @@ resolve_data <- function(x, y) {
 #' @param learn_rate initial learning rate for the optimizer.
 #' @param optimizer the optimization method. currently only 'adam' is supported,
 #'   you can also pass any torch optimizer function.
-#' @param valid_split (float) The fraction of the dataset used for validation.
+#' @param valid_split (`[0, 1)`) The fraction of the dataset used for validation.
 #'   (default = 0 means no split)
-#' @param num_independent Number of independent Gated Linear Units layers at each step.
+#' @param num_independent Number of independent Gated Linear Units layers at each step of the encoder.
 #'   Usual values range from 1 to 5.
-#' @param num_shared Number of shared Gated Linear Units at each step Usual values
-#'   range from 1 to 5
+#' @param num_shared Number of shared Gated Linear Units at each step of the encoder. Usual values
+#'    at each step of the decoder. range from 1 to 5
+#' @param num_independent_decoder For pretraining, number of independent Gated Linear Units layers
+#'   Usual values range from 1 to 5.
+#' @param num_shared_decoder For pretraining, number of shared Gated Linear Units at each step of the
+#'    decoder. Usual values range from 1 to 5.
 #' @param verbose (logical) Whether to print progress and loss values during
 #'   training.
 #' @param lr_scheduler if `NULL`, no learning rate decay is used. If "step"
@@ -101,7 +105,9 @@ resolve_data <- function(x, y) {
 #'   or `NULL`.
 #' @param step_size the learning rate scheduler step size. Unused if
 #'   `lr_scheduler` is a `torch::lr_scheduler` or `NULL`.
-#' @param cat_emb_dim Embedding size for categorical features (default=1)
+#' @param cat_emb_dim Size of the embedding of categorical features. If int, all categorical
+#'   features will have same embedding size, if list of int, every corresponding feature will have
+#'   specific embedding size.
 #' @param momentum Momentum for batch normalization, typically ranges from 0.01
 #'   to 0.4 (default=0.02)
 #' @param pretraining_ratio Ratio of features to mask for reconstruction during
@@ -147,6 +153,8 @@ tabnet_config <- function(batch_size = 1024^2,
                           cat_emb_dim = 1,
                           num_independent = 2,
                           num_shared = 2,
+                          num_independent_decoder = 1,
+                          num_shared_decoder = 1,
                           momentum = 0.02,
                           pretraining_ratio = 0.5,
                           verbose = FALSE,
@@ -190,6 +198,8 @@ tabnet_config <- function(batch_size = 1024^2,
     cat_emb_dim = cat_emb_dim,
     n_independent = num_independent,
     n_shared = num_shared,
+    n_independent_decoder = num_independent_decoder,
+    n_shared_decoder = num_shared_decoder,
     momentum = momentum,
     pretraining_ratio = pretraining_ratio,
     verbose = verbose,
@@ -198,7 +208,7 @@ tabnet_config <- function(batch_size = 1024^2,
     early_stopping_monitor = resolve_early_stop_monitor(early_stopping_monitor, valid_split),
     early_stopping_tolerance = early_stopping_tolerance,
     early_stopping_patience = early_stopping_patience,
-    early_stopping = !(early_stopping_tolerance==0 || early_stopping_patience==0),
+    early_stopping = !(early_stopping_tolerance == 0 || early_stopping_patience == 0),
     num_workers = num_workers,
     skip_importance = skip_importance
   )

R/pretraining.R

@@ -119,6 +119,8 @@ tabnet_train_unsupervised <- function(x, config = tabnet_config(), epoch_shift =
     cat_emb_dim = config$cat_emb_dim,
     n_independent = config$n_independent,
     n_shared = config$n_shared,
+    n_independent_decoder = config$n_independent_decoder,
+    n_shared_decoder = config$n_shared_decoder,
     momentum = config$momentum
   )
 

R/tab-network.R

@@ -234,14 +234,15 @@ tabnet_decoder <- torch::nn_module(
 
 tabnet_pretrainer <- torch::nn_module(
   "tabnet_pretrainer",
-  initialize = function(input_dim, pretraining_ratio=0.2,
-                        n_d=8, n_a=8,
-                        n_steps=3, gamma=1.3,
-                        cat_idxs=c(), cat_dims=c(),
-                        cat_emb_dim=1, n_independent=2,
-                        n_shared=2, epsilon=1e-15,
-                        virtual_batch_size=128, momentum = 0.02,
-                        mask_type="sparsemax") {
+  initialize = function(input_dim, pretraining_ratio = 0.2,
+                        n_d = 8, n_a = 8,
+                        n_steps = 3, gamma = 1.3,
+                        cat_idxs = c(), cat_dims = c(),
+                        cat_emb_dim = 1, n_independent = 2,
+                        n_shared = 2, n_independent_decoder = 1,
+                        n_shared_decoder = 1, epsilon = 1e-15,
+                        virtual_batch_size = 128, momentum = 0.02,
+                        mask_type = "sparsemax") {
 
     self$input_dim <- input_dim
     self$pretraining_ratio <- pretraining_ratio
@@ -259,6 +260,8 @@ tabnet_pretrainer <- torch::nn_module(
     self$epsilon <- epsilon
     self$n_independent <- n_independent
     self$n_shared <- n_shared
+    self$n_independent_decoder <- n_independent_decoder
+    self$n_shared_decoder <- n_shared_decoder
     self$mask_type <- mask_type
     self$initial_bn <- torch::nn_batch_norm1d(self$input_dim, momentum = momentum)
 
@@ -290,8 +293,8 @@ tabnet_pretrainer <- torch::nn_module(
       self$post_embed_dim,
       n_d = n_d,
       n_steps = n_steps,
-      n_independent = n_independent,
-      n_shared = n_shared,
+      n_independent = n_independent_decoder,
+      n_shared = n_shared_decoder,
       virtual_batch_size = virtual_batch_size,
       momentum = momentum
     )
@@ -409,14 +412,14 @@ tabnet_no_embedding <- torch::nn_module(
 #' @param n_d Dimension of the prediction  layer (usually between 4 and 64).
 #' @param n_a Dimension of the attention  layer (usually between 4 and 64).
 #' @param n_steps Number of successive steps in the network (usually between 3 and 10).
-#' @param gamma Float above 1, scaling factor for attention updates (usually between 1.0 to 2.0).
+#' @param gamma Float above 1, scaling factor for attention updates (usually between 1 and 2).
 #' @param cat_idxs Index of each categorical column in the dataset.
 #' @param cat_dims Number of categories in each categorical column.
 #' @param cat_emb_dim Size of the embedding of categorical features if int, all categorical
 #'   features will have same embedding size if list of int, every corresponding feature will have
 #'   specific size.
-#' @param n_independent Number of independent GLU layer in each GLU block (default 2)..
-#' @param n_shared Number of independent GLU layer in each GLU block (default 2).
+#' @param n_independent Number of independent GLU layer in each GLU block of the encoder.
+#' @param n_shared Number of independent GLU layer in each GLU block of the encoder.
 #' @param epsilon Avoid log(0), this should be kept very low.
 #' @param virtual_batch_size Batch size for Ghost Batch Normalization.
 #' @param momentum  Float value between 0 and 1 which will be used for momentum in all batch norm.