Merge pull request #48 from mlverse/release/v0.1.0

Release/v0.1.0

.Rbuildignore

@@ -8,3 +8,5 @@
 ^pkgdown$
 ^doc$
 ^Meta$
+^cran-comments\.md$
+^CRAN-RELEASE$

CRAN-RELEASE

@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2021-06-16.
+Once it is accepted, delete this file and tag the release (commit 1d00ae2).

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: luz
 Title: Higher Level 'API' for 'torch'
-Version: 0.0.0.9000
+Version: 0.1.0
 Authors@R: c(
     person("Daniel", "Falbel", email = "[email protected]", role = c("aut", "cre", "cph")),
     person(family = "RStudio", role = c("cph"))
@@ -12,9 +12,8 @@ Description: A high level interface for 'torch' providing utilities to reduce th
     Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 
     'Pytorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
 License: MIT + file LICENSE
-URL: https://mlverse.github.io/luz, https://github.com/mlverse/luz
+URL: https://mlverse.github.io/luz/, https://github.com/mlverse/luz
 Encoding: UTF-8
-LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
 Imports: 

R/callbacks-interrupt.R

@@ -10,6 +10,12 @@ NULL
 #' @note In general you don't need to use these callback by yourself because it's always
 #'   included by default in [fit.luz_module_generator()].
 #'
+#' @examples
+#' interrupt_callback <- luz_callback_interrupt()
+#'
+#' @returns
+#' A `luz_callback`
+#'
 #' @family luz_callbacks
 #' @export
 luz_callback_interrupt <- luz_callback(

R/callbacks-profile.R

@@ -16,6 +16,12 @@
 #' @note In general you don't need to use these callback by yourself because it's always
 #'   included by default in [fit.luz_module_generator()].
 #'
+#' @examples
+#' profile_callback <- luz_callback_profile()
+#'
+#' @returns
+#' A `luz_callback`
+#'
 #' @family luz_callbacks
 #' @export
 luz_callback_profile <- luz_callback(

R/callbacks.R

@@ -86,6 +86,10 @@ luz_callback <- function(name = NULL, ..., private = NULL, active = NULL, parent
 #' @note Printing can be disabled by passing `verbose=FALSE` to [fit.luz_module_generator()].
 #'
 #' @family luz_callbacks
+#'
+#' @returns
+#' A `luz_callback`
+#'
 #' @export
 luz_callback_progress <- luz_callback(
   "progress_callback",
@@ -203,6 +207,10 @@ luz_callback_progress <- luz_callback(
 #' used by default in [fit.luz_module_generator()].
 #'
 #' @family luz_callbacks
+#'
+#' @returns
+#' A `luz_callback`
+#'
 #' @export
 luz_callback_metrics <- luz_callback(
   "metrics_callback",
@@ -272,6 +280,9 @@ luz_callback_metrics <- luz_callback(
 #' @note In general you won't need to explicitly use the metrics callback as it's
 #' used by default in [fit.luz_module_generator()].
 #'
+#' @returns
+#' A `luz_callback`
+#'
 #' @family luz_callbacks
 #' @export
 luz_callback_train_valid <- luz_callback(

R/metrics.R

@@ -96,6 +96,11 @@ luz_metric <- function(name = NULL, ..., private = NULL, active = NULL,
 #' metric$compute()
 #' }
 #' @export
+#'
+#'
+#' @returns
+#' Returns new Luz metric.
+#'
 #' @family luz_metrics
 luz_metric_accuracy <- luz_metric(
   abbrev = "Acc",
@@ -131,6 +136,10 @@ luz_metric_accuracy <- luz_metric(
 #' metric$update(torch_rand(100), torch::torch_randint(0, 1, size = 100))
 #' metric$compute()
 #' }
+#'
+#' @returns
+#' Returns new Luz metric.
+#'
 #' @family luz_metrics
 #' @export
 luz_metric_binary_accuracy <- luz_metric(
@@ -172,6 +181,8 @@ luz_metric_binary_accuracy <- luz_metric(
 #' metric$update(torch_randn(100), torch::torch_randint(0, 1, size = 100))
 #' metric$compute()
 #' }
+#' @returns
+#' Returns new Luz metric.
 #'
 #' @family luz_metrics
 #' @export
@@ -240,6 +251,8 @@ luz_metric_loss_average <- luz_metric(
 #' metric$update(torch_randn(100), torch_randn(100))
 #' metric$compute()
 #' }
+#' @returns
+#' Returns new Luz metric.
 #'
 #' @family luz_metrics
 #' @export
@@ -289,6 +302,10 @@ luz_metric_mse <- luz_metric(
 #' Computes the root mean squared error.
 #'
 #' @family luz_metrics
+#'
+#' @returns
+#' Returns new Luz metric.
+#'
 #' @export
 luz_metric_rmse <- luz_metric(
   inherit = luz_metric_mse,

R/module-plot.R

@@ -5,3 +5,5 @@ plot.luz_module_fitted <- function(x, ...) {
   p <- p + ggplot2::geom_point() + ggplot2::geom_line()
   p + ggplot2::facet_grid(metric ~ set, scales = "free_y")
 }
+
+globalVariables(c("epoch", "value"))

R/module-print.R

@@ -43,3 +43,5 @@ print.luz_module_fitted <- function(x, ...) {
   print(x$model)
 }
 
+
+

R/module.R

@@ -16,6 +16,9 @@
 #' @param metrics (`list`, optional) A list of metrics to be tracked during
 #' the training procedure.
 #'
+#' @returns
+#' A luz module that can be trained with [fit()].
+#'
 #' @family training
 #'
 #' @export
@@ -68,6 +71,9 @@ setup <- function(module, loss = NULL, optimizer = NULL, metrics = NULL) {
 #'
 #' @family set_hparam
 #'
+#' @returns
+#' The same luz module
+#'
 #' @export
 set_hparams <- function(module, ...) {
   hparams <- rlang::list2(...)
@@ -87,6 +93,9 @@ set_hparams <- function(module, ...) {
 #' `optim_adam` function is called with `optim_adam(parameters, lr=0.1)` when fitting
 #' the model.
 #'
+#' @returns
+#' The same luz module
+#'
 #' @family set_hparam
 #' @export
 set_opt_hparams <- function(module, ...) {
@@ -132,9 +141,12 @@ get_opt_hparams <- function(module) {
 #'
 #' @param ... Currently unused,
 #'
+#' @returns
+#' A fitted object that can be saved with [luz_save()] and can be printed with
+#' [print()] and plotted with [plot()].
+#'
 #' @importFrom generics fit
 #' @export
-#'
 fit.luz_module_generator <- function(object, data, epochs = 10, callbacks = NULL,
                                      valid_data = NULL, accelerator = NULL,
                                      verbose = NULL, ...) {

README.md

@@ -3,7 +3,7 @@
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/mlverse/luz/workflows/R-CMD-check/badge.svg)](https://github.com/mlverse/luz/actions)
 [![Codecov test coverage](https://codecov.io/gh/mlverse/luz/branch/master/graph/badge.svg)](https://codecov.io/gh/mlverse/luz?branch=master)
-[![Discord](https://img.shields.io/discord/837019024499277855?logo=discord)](https://discord.gg/s3D5cKhBkx)
+[![Discord](https://img.shields.io/discord/837019024499277855?logo=discord)](https://discord.com/invite/s3D5cKhBkx)
 <!-- badges: end -->
 
 luz is a higher level API for torch providing abstractions to allow for much less verbose training loops.

cran-comments.md

@@ -0,0 +1 @@
+First release.

tests/testthat.R

@@ -1,4 +1,6 @@
 library(testthat)
 library(luz)
 
-test_check("luz")
+if (Sys.getenv("TORCH_TEST", unset = 0) == 1)
+  test_check("luz")
+