Release 0.6.0 (#158)

* register S3 nn_prune_head

* Increment version number to 0.6.0

* fix `could not find function data()`

.Rbuildignore

@@ -11,3 +11,5 @@
 ^.V8*
 ^doc$
 ^Meta$
+^CRAN-SUBMISSION$
+^revdep$

.gitignore

@@ -8,3 +8,7 @@ activate
 /doc/
 /Meta/
 revdep
+revdep/
+tabnet.Rcheck
+..Rcheck
+tabnet_*.tar.gz

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tabnet
 Title: Fit 'TabNet' Models for Classification and Regression
-Version: 0.5.0
+Version: 0.6.0
 Authors@R: c(
     person("Daniel", "Falbel", , "[email protected]", role = "aut"),
     person(, "RStudio", role = "cph"),
@@ -11,8 +11,8 @@ Authors@R: c(
            comment = c(ORCID = "0000-0002-5573-3952"))
   )
 Description: Implements the 'TabNet' model by Sercan O. Arik et al. (2019)
-    <arXiv:1908.07442> with 'Coherent Hierarchical Multi-label
-    Classification Networks' by Giunchiglia et al.  <arXiv:2010.10151> and
+    <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label
+    Classification Networks' by Giunchiglia et al.  <doi:10.48550/arXiv.2010.10151> and
     provides a consistent interface for fitting and creating predictions.
     It's also fully compatible with the 'tidymodels' ecosystem.
 License: MIT + file LICENSE
@@ -51,6 +51,7 @@ Suggests:
     recipes,
     rmarkdown,
     rsample,
+    spelling,
     testthat (>= 3.0.0),
     tidymodels,
     tidyverse,
@@ -66,3 +67,4 @@ Config/testthat/start-first: interface, explain, params
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
+Language: en-US

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(multi_predict,"_tabnet_fit")
 S3method(predict,tabnet_fit)
 S3method(print,tabnet_fit)
 S3method(print,tabnet_pretrain)
@@ -46,6 +47,7 @@ importFrom(dplyr,select)
 importFrom(dplyr,starts_with)
 importFrom(dplyr,where)
 importFrom(magrittr,"%>%")
+importFrom(parsnip,multi_predict)
 importFrom(rlang,.data)
 importFrom(stats,predict)
 importFrom(stats,update)

R/package.R

@@ -5,11 +5,14 @@
   vctrs::s3_register("ggplot2::autoplot", "tabnet_fit")
   vctrs::s3_register("ggplot2::autoplot", "tabnet_pretrain")
   vctrs::s3_register("ggplot2::autoplot", "tabnet_explain")
+  vctrs::s3_register("torch::nn_prune_head", "tabnet_fit")
+  vctrs::s3_register("torch::nn_prune_head", "tabnet_pretrain")
   vctrs::s3_register("tune::min_grid", "tabnet")
 }
 
 
 globalVariables(c("batch_size",
+                  "checkpoint",
                   "dataset",
                   "epoch",
                   "has_checkpoint",

R/parsnip.R

@@ -459,11 +459,12 @@ tabnet <- function(mode = "unknown",  cat_emb_dim = NULL, decision_width = NULL,
   if (!requireNamespace("parsnip", quietly = TRUE))
     stop("Package \"parsnip\" needed for this function to work. Please install it.", call. = FALSE)
 
-  if (!tabnet_env$parsnip_added) {
+  if (parsnip_is_missing_tabnet(tabnet_env)) {
     add_parsnip_tabnet()
     tabnet_env$parsnip_added <- TRUE
   }
 
+
   # Capture the arguments in quosures
   args <- list(
     cat_emb_dim = rlang::enquo(cat_emb_dim),
@@ -512,7 +513,8 @@ tabnet <- function(mode = "unknown",  cat_emb_dim = NULL, decision_width = NULL,
 tabnet_env <- new.env()
 tabnet_env$parsnip_added <- FALSE
 
-
+#' @export
+#' @importFrom parsnip multi_predict
 multi_predict._tabnet_fit <- function(object, new_data, type = NULL, epochs = NULL, ...) {
 
   if (is.null(epochs))
@@ -567,3 +569,8 @@ update.tabnet <- function(object, parameters = NULL, epochs = NULL, penalty = NU
 }
 
 min_grid.tabnet <- function(x, grid, ...) tune::fit_max_value(x, grid, ...)
+
+parsnip_is_missing_tabnet <- function(tabnet_env) {
+  current <- parsnip::get_model_env()
+  !(any(current$models == "tabnet") || tabnet_env$parsnip_added)
+}

R/plot.R

@@ -40,8 +40,8 @@ autoplot.tabnet_fit <- function(object, ...) {
 
   if ("checkpoint" %in% names(collect_metrics)) {
     checkpoints <- collect_metrics %>%
-      dplyr::filter(.data$checkpoint == TRUE, dataset == "train") %>%
-      dplyr::select(-.data$checkpoint) %>%
+      dplyr::filter(checkpoint == TRUE, dataset == "train") %>%
+      dplyr::select(-checkpoint) %>%
       dplyr::mutate(size = 2)
     p +
       ggplot2::geom_point(data = checkpoints, ggplot2::aes(x = epoch, y = loss, color = dataset, size = .data$size ))

cran-comments.md

@@ -1,5 +1,4 @@
-## R CMD check results
+── R CMD check results ─────────────────────────────────────────────────────────────────── tabnet 0.6.0 ────
+Duration: 2m 13s
 
-0 errors | 0 warnings | 1 note
-
-* This is a new release.
+0 errors ✔ | 0 warnings ✔ | 0 notes ✔

inst/WORDLIST

@@ -1,28 +1,52 @@
+Ames
+Arik
+Bugfixes
+Eleonora
+Explicitely
+GLU
+Giunchiglia
+Interpretability
+Interpretable
+Lifecycle
+MNAR
+OOM
+Pfister
+Pretrain
+Sercan
+TabNet
+TabNet's
 adam
 ai
-al.
+al
 ames
-Ames
-Arik
 arXiv
 autograd
+beeing
+classif
+config
+cpu
+cuda
+dataloading
+detailled
+doi
 dreamquark
+entmax
 et
-Giunchiglia
-giunchiglia
+explainability
+ggplot
 interpretable
-Interpretable
-Lifecycle
 mse
-neurips
-NMAR
+nn
+orginal
 overfit
 overfits
 pre
 pretrain
 pretrained
 pretraining
-PyTorch
-Sercan
-TabNet
+reusage
+sparsemax
+subprocesses
+th
 tidymodels
+zeallot

tests/testthat/setup.R

@@ -5,7 +5,7 @@ suppressPackageStartupMessages(library(data.tree))
 
 
 # ames small data
-data("ames", package = "modeldata")
+utils::data("ames", package = "modeldata")
 ids <- sample(nrow(ames), 256)
 small_ames <- ames[ids,]
 x <- ames[ids,-which(names(ames) == "Sale_Price")]
@@ -20,7 +20,7 @@ ames_fit_vsplit <- tabnet_fit(x, y, tabnet_model=ames_pretrain_vsplit, epochs =
                               num_steps = 1, attention_width = 1, num_shared = 1, num_independent = 1)
 
 # attrition small data
-data("attrition", package = "modeldata")
+utils::data("attrition", package = "modeldata")
 ids <- sample(nrow(attrition), 256)
 
 # attrition common models
@@ -35,7 +35,7 @@ attr_fitted <- tabnet_fit(attrix, attriy, epochs = 12)
 attr_fitted_vsplit <- tabnet_fit(attrix, attriy, epochs = 12, valid_split=0.3)
 
 # data.tree Node dataset
-data("acme", package = "data.tree")
+utils::data("acme", package = "data.tree")
 acme_df <-  data.tree::ToDataFrameTypeCol(acme, acme$attributesAll) %>%
   select(-starts_with("level_"))
 
@@ -46,4 +46,4 @@ attrition_tree <- attrition %>%
   data.tree::as.Node()
 
 # Run after all tests
-withr::defer(teardown_env())
+withr::defer(testthat::teardown_env())