[docs] use consistent naming for R-package and Python-package across the repo (#6655)

.ci/check-python-dists.sh

@@ -8,7 +8,7 @@ DIST_DIR=${1}
 METHOD=${METHOD:-""}
 TASK=${TASK:-""}
 
-echo "checking Python package distributions in '${DIST_DIR}'"
+echo "checking Python-package distributions in '${DIST_DIR}'"
 
 pip install \
     -qq \
@@ -55,4 +55,4 @@ else
     echo "skipping pydistcheck (does not support Python 3.${PY_MINOR_VER})"
 fi
 
-echo "done checking Python package distributions"
+echo "done checking Python-package distributions"

.ci/test-python-oldest.sh

@@ -30,7 +30,7 @@ pip freeze
 echo ""
 echo "checking that examples run without error"
 
-# run a few examples to test that Python package minimally works
+# run a few examples to test that Python-package minimally works
 echo ""
 echo "--- advanced_example.py ---"
 echo ""

.ci/test-r-package-windows.ps1

@@ -144,7 +144,7 @@ Write-Output "Installing dependencies"
 $packages = "c('data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'processx', 'R6', 'RhpcBLASctl', 'testthat'), dependencies = c('Imports', 'Depends', 'LinkingTo')"
 Run-R-Code-Redirect-Stderr "options(install.packages.check.source = 'no'); install.packages($packages, repos = '$env:CRAN_MIRROR', type = 'binary', lib = '$env:R_LIB_PATH', Ncpus = parallel::detectCores())" ; Check-Output $?
 
-Write-Output "Building R package"
+Write-Output "Building R-package"
 
 # R CMD check is not used for MSVC builds
 if ($env:COMPILER -ne "MSVC") {
@@ -234,7 +234,7 @@ if ($checks_cnt -eq 0) {
   Check-Output $False
 }
 
-# Checking that we actually got the expected compiler. The R package has some logic
+# Checking that we actually got the expected compiler. The R-package has some logic
 # to fail back to MinGW if MSVC fails, but for CI builds we need to check that the correct
 # compiler was used.
 if ($env:R_BUILD_TYPE -eq "cmake") {
@@ -303,4 +303,4 @@ if ($env:COMPILER -eq "MSVC") {
   Rscript.exe --vanilla "testthat.R" ; Check-Output $?
 }
 
-Write-Output "No issues were found checking the R package"
+Write-Output "No issues were found checking the R-package"

.ci/test-r-package.sh

@@ -155,7 +155,7 @@ elif [[ $R_BUILD_TYPE == "cran" ]]; then
             git diff --name-only | wc -l
         )
         if [[ ${num_files_changed} -gt 0 ]]; then
-            echo "'configure' in the R package has changed. Please recreate it and commit the changes."
+            echo "'configure' in the R-package has changed. Please recreate it and commit the changes."
             echo "Changed files:"
             git diff --compact-summary
             echo "See R-package/README.md for details on how to recreate this script."
@@ -167,7 +167,7 @@ elif [[ $R_BUILD_TYPE == "cran" ]]; then
     ./build-cran-package.sh || exit 1
 
     if [[ "${TASK}" == "r-rchk" ]]; then
-        echo "Checking R package with rchk"
+        echo "Checking R-package with rchk"
         mkdir -p packages
         cp ${PKG_TARBALL} packages
         RCHK_LOG_FILE="rchk-logs.txt"

CMakeLists.txt

@@ -19,8 +19,8 @@ option(BUILD_CLI "Build the 'lightbgm' command-line interface in addition to lib
 option(BUILD_CPP_TEST "Build C++ tests with Google Test" OFF)
 option(BUILD_STATIC_LIB "Build static library" OFF)
 option(INSTALL_HEADERS "Install headers to CMAKE_INSTALL_PREFIX (e.g. '/usr/local/include')" ON)
-option(__BUILD_FOR_PYTHON "Set to ON if building lib_lightgbm for use with the Python package" OFF)
-option(__BUILD_FOR_R "Set to ON if building lib_lightgbm for use with the R package" OFF)
+option(__BUILD_FOR_PYTHON "Set to ON if building lib_lightgbm for use with the Python-package" OFF)
+option(__BUILD_FOR_R "Set to ON if building lib_lightgbm for use with the R-package" OFF)
 option(__INTEGRATE_OPENCL "Set to ON if building LightGBM with the OpenCL ICD Loader and its dependencies included" OFF)
 
 cmake_minimum_required(VERSION 3.28)
@@ -60,9 +60,9 @@ if(__INTEGRATE_OPENCL)
 endif()
 
 if(__BUILD_FOR_PYTHON OR __BUILD_FOR_R OR USE_SWIG)
-    # the SWIG wrapper, the Python and R package don't require the CLI
+    # the SWIG wrapper, the Python and R packages don't require the CLI
     set(BUILD_CLI OFF)
-    # installing the SWIG wrapper, the R and Python package shouldn't place LightGBM's headers
+    # installing the SWIG wrapper, the R and Python packages shouldn't place LightGBM's headers
     # outside of where the package is installed
     set(INSTALL_HEADERS OFF)
 endif()
@@ -497,7 +497,7 @@ endif()
 
 set(API_SOURCES "src/c_api.cpp")
 # Only build the R part of the library if building for
-# use with the R package
+# use with the R-package
 if(__BUILD_FOR_R)
   list(APPEND API_SOURCES "src/lightgbm_R.cpp")
 endif()

R-package/README.md

@@ -112,7 +112,7 @@ NOTE: `Rtools42` and later take a very different approach to the compiler toolch
 
 #### Windows Toolchain Options
 
-A "toolchain" refers to the collection of software used to build the library. The R package can be built with three different toolchains.
+A "toolchain" refers to the collection of software used to build the library. The R-package can be built with three different toolchains.
 
 **Warning for Windows users**: it is recommended to use *Visual Studio* for its better multi-threading efficiency in Windows for many core systems. For very simple systems (dual core computers or worse), MinGW64 is recommended for maximum performance. If you do not know what to choose, it is recommended to use [Visual Studio](https://visualstudio.microsoft.com/downloads/), the default compiler. **Do not try using MinGW in Windows on many core systems. It may result in 10x slower results than Visual Studio.**
 
@@ -221,7 +221,7 @@ CRAN does not prepare precompiled binaries for Linux, and as of this writing nei
 
 ### Installing from a Pre-compiled lib_lightgbm <a name="lib_lightgbm"></a>
 
-Previous versions of LightGBM offered the ability to first compile the C++ library (`lib_lightgbm.{dll,dylib,so}`) and then build an R package that wraps it.
+Previous versions of LightGBM offered the ability to first compile the C++ library (`lib_lightgbm.{dll,dylib,so}`) and then build an R-package that wraps it.
 
 As of version 3.0.0, this is no longer supported. If building from source is difficult for you, please [open an issue](https://github.com/microsoft/LightGBM/issues).
 
@@ -241,11 +241,11 @@ Please visit [demo](https://github.com/microsoft/LightGBM/tree/master/R-package/
 Testing
 -------
 
-The R package's unit tests are run automatically on every commit, via integrations like [GitHub Actions](https://github.com/microsoft/LightGBM/actions). Adding new tests in `R-package/tests/testthat` is a valuable way to improve the reliability of the R package.
+The R-package's unit tests are run automatically on every commit, via integrations like [GitHub Actions](https://github.com/microsoft/LightGBM/actions). Adding new tests in `R-package/tests/testthat` is a valuable way to improve the reliability of the R-package.
 
 ### Running the Tests
 
-While developing the R package, run the code below to run the unit tests.
+While developing the R-package, run the code below to run the unit tests.
 
 ```shell
 sh build-cran-package.sh \
@@ -268,7 +268,7 @@ Rscript testthat.R
 
 When adding tests, you may want to use test coverage to identify untested areas and to check if the tests you've added are covering all branches of the intended code.
 
-The example below shows how to generate code coverage for the R package on a macOS or Linux setup. To adjust for your environment, refer to [the customization step described above](#custom-installation-linux-mac).
+The example below shows how to generate code coverage for the R-package on a macOS or Linux setup. To adjust for your environment, refer to [the customization step described above](#custom-installation-linux-mac).
 
 ```shell
 # Install
@@ -287,7 +287,7 @@ Rscript -e " \
 Updating Documentation
 ----------------------
 
-The R package uses [`{roxygen2}`](https://CRAN.R-project.org/package=roxygen2) to generate its documentation.
+The R-package uses [`{roxygen2}`](https://CRAN.R-project.org/package=roxygen2) to generate its documentation.
 The generated `DESCRIPTION`, `NAMESPACE`, and `man/` files are checked into source control.
 To regenerate those files, run the following.
 
@@ -310,9 +310,9 @@ Rscript \
 Preparing a CRAN Package
 ------------------------
 
-This section is primarily for maintainers, but may help users and contributors to understand the structure of the R package.
+This section is primarily for maintainers, but may help users and contributors to understand the structure of the R-package.
 
-Most of `LightGBM` uses `CMake` to handle tasks like setting compiler and linker flags, including header file locations, and linking to other libraries. Because CRAN packages typically do not assume the presence of `CMake`, the R package uses an alternative method that is in the CRAN-supported toolchain for building R packages with C++ code: `Autoconf`.
+Most of `LightGBM` uses `CMake` to handle tasks like setting compiler and linker flags, including header file locations, and linking to other libraries. Because CRAN packages typically do not assume the presence of `CMake`, the R-package uses an alternative method that is in the CRAN-supported toolchain for building R packages with C++ code: `Autoconf`.
 
 For more information on this approach, see ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup).
 
@@ -344,7 +344,7 @@ R CMD install lightgbm_*.tar.gz
 
 ### Changing the CRAN Package
 
-A lot of details are handled automatically by `R CMD build` and `R CMD install`, so it can be difficult to understand how the files in the R package are related to each other. An extensive treatment of those details is available in ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html).
+A lot of details are handled automatically by `R CMD build` and `R CMD install`, so it can be difficult to understand how the files in the R-package are related to each other. An extensive treatment of those details is available in ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html).
 
 This section briefly explains the key files for building a CRAN package. To update the package, edit the files relevant to your change and re-run the steps in [Build a CRAN Package](#build-a-cran-package).
 
@@ -488,4 +488,4 @@ These tests can also be triggered on any pull request by leaving a comment in a
 Known Issues
 ------------
 
-For information about known issues with the R package, see the [R-package section of LightGBM's main FAQ page](https://lightgbm.readthedocs.io/en/latest/FAQ.html#r-package).
+For information about known issues with the R-package, see the [R-package section of LightGBM's main FAQ page](https://lightgbm.readthedocs.io/en/latest/FAQ.html#r-package).

R-package/tests/testthat/test_basic.R

@@ -634,7 +634,7 @@ test_that("lgb.cv() prefers objective in params to keyword argument", {
   for (bst_list in cv_bst$boosters) {
     bst <- bst_list[["booster"]]
     expect_equal(bst$params$objective, "regression_l1")
-    # NOTE: using save_model_to_string() since that is the simplest public API in the R package
+    # NOTE: using save_model_to_string() since that is the simplest public API in the R-package
     #       allowing access to the "objective" attribute of the Booster object on the C++ side
     model_txt_lines <- strsplit(
       x = bst$save_model_to_string()
@@ -807,7 +807,7 @@ test_that("lgb.train() prefers objective in params to keyword argument", {
     , obj = "regression_l2"
   )
   expect_equal(bst$params$objective, "regression_l1")
-  # NOTE: using save_model_to_string() since that is the simplest public API in the R package
+  # NOTE: using save_model_to_string() since that is the simplest public API in the R-package
   #       allowing access to the "objective" attribute of the Booster object on the C++ side
   model_txt_lines <- strsplit(
     x = bst$save_model_to_string()

R-package/tests/testthat/test_lgb.Booster.R

@@ -1111,7 +1111,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
     )
     all_param_entries <- c(non_default_param_entries, default_param_entries)
 
-    # parameters should match what was passed from the R package
+    # parameters should match what was passed from the R-package
     model_str <- bst$save_model_to_string()
     params_in_file <- .params_from_model_string(model_str = model_str)
     .expect_in(all_param_entries, params_in_file)
@@ -1176,7 +1176,7 @@ test_that("early_stopping, num_iterations are stored correctly in model string e
     model_str <- bst$save_model_to_string()
     params_in_file <- .params_from_model_string(model_str = model_str)
 
-    # parameters should match what was passed from the R package, and the "main" (non-alias)
+    # parameters should match what was passed from the R-package, and the "main" (non-alias)
     # params values in `params` should be preferred to keyword argumentts or aliases
     expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L)
     expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L)
@@ -1406,7 +1406,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
         model_str <- bst[["booster"]]$save_model_to_string()
         params_in_file <- .params_from_model_string(model_str = model_str)
 
-        # parameters should match what was passed from the R package, and the "main" (non-alias)
+        # parameters should match what was passed from the R-package, and the "main" (non-alias)
         # params values in `params` should be preferred to keyword argumentts or aliases
         expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L)
         expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L)

R-package/vignettes/basic_walkthrough.Rmd

@@ -54,7 +54,7 @@ table(bank$y)
 
 ## Training the model
 
-The R package of LightGBM offers two functions to train a model:
+The R-package of LightGBM offers two functions to train a model:
 
 - `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function.
 - `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`.

build-cran-package.sh

 #!/bin/sh
 
 # [description]
-#     Prepare a source distribution of the R package
+#     Prepare a source distribution of the R-package
 #     to be submitted to CRAN.
 #
 # [arguments]
 #
 #     --r-executable Customize the R executable used by `R CMD build`.
-#                    Useful if building the R package in an environment with
+#                    Useful if building the R-package in an environment with
 #                    non-standard builds of R, such as those provided in
 #                    https://github.com/wch/r-debug.
 #
@@ -83,7 +83,7 @@ cp \
     external_libs/fmt/include/fmt/*.h \
     "${TEMP_R_DIR}/src/include/LightGBM/utils/fmt"
 
-# including only specific files from Eigen, to keep the R package
+# including only specific files from Eigen, to keep the R-package
 # small and avoid redistributing code with licenses incompatible with
 # LightGBM's license
 EIGEN_R_DIR="${TEMP_R_DIR}/src/include/Eigen"
@@ -116,7 +116,7 @@ cd "${TEMP_R_DIR}"
     rm recreate-configure.sh
 
     # files only used by the lightgbm CLI aren't needed for
-    # the R package
+    # the R-package
     rm src/application/application.cpp
     rm src/include/LightGBM/application.h
     rm src/main.cpp
@@ -223,4 +223,4 @@ else
         lightgbm_r
 fi
 
-echo "Done building R package"
+echo "Done building R-package"

build-python.sh

@@ -3,7 +3,7 @@
 # [description]
 #
 #     Prepare a source distribution (sdist) or built distribution (wheel)
-#     of the Python package, and optionally install it.
+#     of the Python-package, and optionally install it.
 #
 # [usage]
 #
@@ -13,10 +13,10 @@
 #     # build wheel and put it in dist/
 #     sh ./build-python.sh bdist_wheel [OPTIONS]
 #
-#     # compile lib_lightgbm and install the Python package wrapping it
+#     # compile lib_lightgbm and install the Python-package wrapping it
 #     sh ./build-python.sh install [OPTIONS]
 #
-#     # install the Python package using a pre-compiled lib_lightgbm
+#     # install the Python-package using a pre-compiled lib_lightgbm
 #     # (assumes lib_lightgbm.{dll,so} is located at the root of the repo)
 #     sh ./build-python.sh install --precompile
 #
@@ -184,7 +184,7 @@ done
 pip install --prefer-binary 'build>=0.10.0'
 
 # create a new directory that just contains the files needed
-# to build the Python package
+# to build the Python-package
 create_isolated_source_dir() {
     rm -rf \
         ./lightgbm-python \

build_r.R

@@ -321,7 +321,7 @@ for (submodule in list.dirs(
   , recursive = FALSE
 )) {
   # compute/ is a submodule with boost, only needed if
-  # building the R package with GPU support;
+  # building the R-package with GPU support;
   # eigen/ has a special treatment due to licensing aspects
   if ((submodule == "compute" && !USING_GPU) || submodule == "eigen") {
     next

cmake/modules/FindLibR.cmake

@@ -24,7 +24,7 @@ if(NOT R_ARCH)
 endif()
 
 if(NOT ("${R_ARCH}" STREQUAL "x64"))
-  message(FATAL_ERROR "LightGBM's R package currently only supports 64-bit operating systems")
+  message(FATAL_ERROR "LightGBM's R-package currently only supports 64-bit operating systems")
 endif()
 
 # Creates R.lib and R.def in the build directory for linking with MSVC

docker/README.md

@@ -57,7 +57,7 @@ For more details on how to configure and use the LightGBM CLI, see https://light
 
 ## Running the Python-package Сontainer
 
-Build an image with the LightGBM Python package installed.
+Build an image with the LightGBM Python-package installed.
 
 ```shell
 mkdir lightgbm-docker
@@ -69,8 +69,8 @@ docker build \
     .
 ```
 
-Once that completes, the built image can be used to run LightGBM's Python package in a container.
-Run the following to produce a model using the Python package.
+Once that completes, the built image can be used to run LightGBM's Python-package in a container.
+Run the following to produce a model using the Python-package.
 
 ```shell
 # get training data
@@ -116,7 +116,7 @@ docker run \
 
 ## Running the R-package Сontainer
 
-Build an image with the LightGBM R package installed.
+Build an image with the LightGBM R-package installed.
 
 ```shell
 mkdir lightgbm-docker
@@ -129,8 +129,8 @@ docker build \
     .
 ```
 
-Once that completes, the built image can be used to run LightGBM's R package in a container.
-Run the following to produce a model using the R package.
+Once that completes, the built image can be used to run LightGBM's R-package in a container.
+Run the following to produce a model using the R-package.
 
 ```shell
 # get training data

docs/FAQ.rst

@@ -187,7 +187,7 @@ You can find LightGBM's logo in different file formats and resolutions `here <ht
 
 **Possible Cause**: This behavior may indicate that you have multiple OpenMP libraries installed on your machine and they conflict with each other, similarly to the ``FAQ #10``.
 
-If you are using any Python package that depends on ``threadpoolctl``, you also may see the following warning in your logs in this case:
+If you are using any Python-package that depends on ``threadpoolctl``, you also may see the following warning in your logs in this case:
 
 .. code-block:: console
 
@@ -228,10 +228,10 @@ which makes such failures more likely. See these discussions:
 * https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6
 * https://gcc.gcc.gnu.narkive.com/vOXMQqLA/failure-to-dlopen-libgomp-due-to-static-tls-data
 
-If you are experiencing this issue when using the ``lightgbm`` Python package, try upgrading
+If you are experiencing this issue when using the ``lightgbm`` Python-package, try upgrading
 to at least ``v4.6.0``.
 
-For older versions of the Python package, or for other LightGBM APIs, this issue can
+For older versions of the Python-package, or for other LightGBM APIs, this issue can
 often be avoided by loading ``libgomp.so.1``. That can be done directly by setting environment
 variable ``LD_PRELOAD``, like this:
 
@@ -261,9 +261,9 @@ R-package
 1. Any training command using LightGBM does not work after an error occurred during the training of a previous LightGBM model.
 ------------------------------------------------------------------------------------------------------------------------------
 
-In older versions of the R package (prior to ``v3.3.0``), this could happen occasionally and the solution was to run ``lgb.unloader(wipe = TRUE)`` to remove all LightGBM-related objects. Some conversation about this could be found in `Microsoft/LightGBM#698 <https://github.com/microsoft/LightGBM/issues/698>`__.
+In older versions of the R-package (prior to ``v3.3.0``), this could happen occasionally and the solution was to run ``lgb.unloader(wipe = TRUE)`` to remove all LightGBM-related objects. Some conversation about this could be found in `Microsoft/LightGBM#698 <https://github.com/microsoft/LightGBM/issues/698>`__.
 
-That is no longer necessary as of ``v3.3.0``, and function ``lgb.unloader()`` has since been removed from the R package.
+That is no longer necessary as of ``v3.3.0``, and function ``lgb.unloader()`` has since been removed from the R-package.
 
 2. I used ``setinfo()``, tried to print my ``lgb.Dataset``, and now the R console froze!
 ----------------------------------------------------------------------------------------

docs/Parallel-Learning-Guide.rst

@@ -60,7 +60,7 @@ Dask
 
 .. versionadded:: 3.2.0
 
-LightGBM's Python package supports distributed learning via `Dask`_. This integration is maintained by LightGBM's maintainers.
+LightGBM's Python-package supports distributed learning via `Dask`_. This integration is maintained by LightGBM's maintainers.
 
 .. warning::
 

python-package/README.rst

@@ -263,7 +263,7 @@ To use ``MSBuild`` (Windows-only), first build ``lib_lightgbm.dll`` by running t
 
   MSBuild.exe windows/LightGBM.sln /p:Configuration=DLL /p:Platform=x64 /p:PlatformToolset=v143
 
-Then install the Python package using that library.
+Then install the Python-package using that library.
 
 .. code:: sh
 

python-package/pyproject.toml

@@ -22,7 +22,7 @@ dependencies = [
     "numpy>=1.17.0",
     "scipy"
 ]
-description = "LightGBM Python Package"
+description = "LightGBM Python-package"
 license = {file = "LICENSE"}
 maintainers = [
   {name = "Yu Shi", email = "yushi@microsoft.com"}

tests/c_api_test/test_.py

@@ -9,7 +9,7 @@ from scipy import sparse
 try:
     from lightgbm.basic import _LIB as LIB
 except ModuleNotFoundError:
-    print("Could not import lightgbm Python package, looking for lib_lightgbm at the repo root")
+    print("Could not import lightgbm Python-package, looking for lib_lightgbm at the repo root")
     if system() in ("Windows", "Microsoft"):
         lib_file = Path(__file__).absolute().parents[2] / "Release" / "lib_lightgbm.dll"
     else: