[docs] add versionadded notes for v4.0.0 features (#5948)

R-package/R/lgb.Booster.R

@@ -843,6 +843,9 @@ Booster <- R6::R6Class(
 #'             passing the prediction type through \code{params} instead of through this argument might
 #'             result in factor levels for classification objectives not being applied correctly to the
 #'             resulting output.
+#'
+#'             \emph{New in version 4.0.0}
+#'
 #' @param start_iteration int or None, optional (default=None)
 #'                        Start index of the iteration to predict.
 #'                        If None or <= 0, starts from the first iteration.
@@ -861,6 +864,9 @@ NULL
 #' @name predict.lgb.Booster
 #' @title Predict method for LightGBM model
 #' @description Predicted values based on class \code{lgb.Booster}
+#'
+#'              \emph{New in version 4.0.0}
+#'
 #' @details If the model object has been configured for fast single-row predictions through
 #'          \link{lgb.configure_fast_predict}, this function will use the prediction parameters
 #'          that were configured for it - as such, extra prediction parameters should not be passed
@@ -878,6 +884,9 @@ NULL
 #'                If single-row predictions are going to be performed frequently, it is recommended to
 #'                pre-configure the model object for fast single-row sparse predictions through function
 #'                \link{lgb.configure_fast_predict}.
+#'
+#'                \emph{Changed from 'data', in version 4.0.0}
+#'
 #' @param header only used for prediction for text file. True if text file has header
 #' @param ... ignored
 #' @return For prediction types that are meant to always return one output per observation (e.g. when predicting
@@ -1137,6 +1146,9 @@ lgb.configure_fast_predict <- function(model,
 #' @name print.lgb.Booster
 #' @title Print method for LightGBM model
 #' @description Show summary information about a LightGBM model object (same as \code{summary}).
+#'
+#'              \emph{New in version 4.0.0}
+#'
 #' @param x Object of class \code{lgb.Booster}
 #' @param ... Not used
 #' @return The same input \code{x}, returned as invisible.
@@ -1186,6 +1198,9 @@ print.lgb.Booster <- function(x, ...) {
 #' @name summary.lgb.Booster
 #' @title Summary method for LightGBM model
 #' @description Show summary information about a LightGBM model object (same as \code{print}).
+#'
+#'              \emph{New in version 4.0.0}
+#'
 #' @param object Object of class \code{lgb.Booster}
 #' @param ... Not used
 #' @return The same input \code{object}, returned as invisible.

R-package/R/lgb.drop_serialized.R

@@ -4,6 +4,9 @@
 #' a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting
 #' serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore,
 #' they can be dropped through this function in order to save memory. Note that the object will be modified in-place.
+#'
+#'              \emph{New in version 4.0.0}
+#'
 #' @param model \code{lgb.Booster} object which was produced with `serializable=TRUE`.
 #'
 #' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible).

R-package/R/lgb.make_serializable.R

@@ -4,6 +4,9 @@
 #' be serializable (e.g. cannot save and load with \code{saveRDS} and \code{readRDS}) as it will lack the raw bytes
 #' needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized
 #' raw bytes and make the object serializable. Note that the object will be modified in-place.
+#'
+#'              \emph{New in version 4.0.0}
+#'
 #' @param model \code{lgb.Booster} object which was produced with `serializable=FALSE`.
 #'
 #' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible).

R-package/R/lgb.restore_handle.R

@@ -5,6 +5,8 @@
 #' object is restored automatically when calling functions such as \code{predict}, but this function can be
 #' used to forcibly restore it beforehand. Note that the object will be modified in-place.
 #'
+#'              \emph{New in version 4.0.0}
+#'
 #' @details Be aware that fast single-row prediction configurations are not restored through this
 #' function. If you wish to make fast single-row predictions using a \code{lgb.Booster} loaded this way,
 #' call \link{lgb.configure_fast_predict} on the loaded \code{lgb.Booster} object.

R-package/R/lightgbm.R

@@ -84,6 +84,9 @@
 #'          Producing and keeping these raw bytes however uses extra memory, and if they are not required,
 #'          it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw
 #'          bytes can be added to the model on demand through function \link{lgb.make_serializable}.
+#'
+#'          \emph{New in version 4.0.0}
+#'
 #' @keywords internal
 NULL
 
@@ -99,6 +102,9 @@ NULL
 #' @param label Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}
 #' @param weights Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all
 #'                observations / rows have the same importance / weight.
+#'
+#'                \emph{Changed from 'weight', in version 4.0.0}
+#'
 #' @param objective Optimization objective (e.g. `"regression"`, `"binary"`, etc.).
 #'                  For a list of accepted objectives, see
 #'                  \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{
@@ -112,7 +118,13 @@ NULL
 #'                  \code{label}).
 #'                  \item Otherwise, will use objective \code{"regression"}.
 #'                  }
+#'
+#'                  \emph{New in version 4.0.0}
+#'
 #' @param init_score initial score is the base prediction lightgbm will boost from
+#'
+#'                   \emph{New in version 4.0.0}
+#'
 #' @param num_threads Number of parallel threads to use. For best speed, this should be set to the number of
 #'                    physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the
 #'                    number of maximum threads.
@@ -129,6 +141,9 @@ NULL
 #'
 #'                    This parameter gets overriden by \code{num_threads} and its aliases under \code{params}
 #'                    if passed there.
+#'
+#'                    \emph{New in version 4.0.0}
+#'
 #' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example
 #'     \itemize{
 #'        \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation}

docs/Parallel-Learning-Guide.rst

@@ -233,6 +233,8 @@ You could edit your firewall rules to allow communication between any of the wor
 Using Custom Objective Functions with Dask
 ******************************************
 
+.. versionadded:: 4.0.0
+
 It is possible to customize the boosting process by providing a custom objective function written in Python.
 See the Dask API's documentation for details on how to implement such functions.
 

docs/Parameters.rst

@@ -145,6 +145,8 @@ Core Parameters
 
    -  ``goss``, Gradient-based One-Side Sampling
 
+   -  *New in 4.0.0*
+
 -  ``data`` :raw-html:`<a id="data" title="Permalink to this parameter" href="#data">&#x1F517;&#xFE0E;</a>`, default = ``""``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename``
 
    -  path of training data, LightGBM will train from this data
@@ -670,6 +672,8 @@ Learning Control Parameters
 
    -  **Note**: can be used only with ``device_type = cpu``
 
+   -  *New in version 4.0.0*
+
 -  ``num_grad_quant_bins`` :raw-html:`<a id="num_grad_quant_bins" title="Permalink to this parameter" href="#num_grad_quant_bins">&#x1F517;&#xFE0E;</a>`, default = ``4``, type = int
 
    -  number of bins to quantization gradients and hessians
@@ -678,6 +682,8 @@ Learning Control Parameters
 
    -  **Note**: can be used only with ``device_type = cpu``
 
+   -  *New in 4.0.0*
+
 -  ``quant_train_renew_leaf`` :raw-html:`<a id="quant_train_renew_leaf" title="Permalink to this parameter" href="#quant_train_renew_leaf">&#x1F517;&#xFE0E;</a>`, default = ``false``, type = bool
 
    -  whether to renew the leaf values with original gradients when quantized training
@@ -686,10 +692,14 @@ Learning Control Parameters
 
    -  **Note**: can be used only with ``device_type = cpu``
 
+   -  *New in 4.0.0*
+
 -  ``stochastic_rounding`` :raw-html:`<a id="stochastic_rounding" title="Permalink to this parameter" href="#stochastic_rounding">&#x1F517;&#xFE0E;</a>`, default = ``true``, type = bool
 
    -  whether to use stochastic rounding in gradient quantization
 
+   -  *New in 4.0.0*
+
 IO Parameters
 -------------
 
@@ -908,6 +918,8 @@ Dataset Parameters
 
    -  **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page <https://github.com/microsoft/lightgbm-transform/issues>`__
 
+   -  *New in 4.0.0*
+
 Predict Parameters
 ~~~~~~~~~~~~~~~~~~
 

include/LightGBM/config.h

@@ -166,6 +166,7 @@ struct Config {
   // desc = ``bagging``, Randomly Bagging Sampling
   // descl2 = **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0``
   // desc = ``goss``, Gradient-based One-Side Sampling
+  // desc = *New in 4.0.0*
   std::string data_sample_strategy = "bagging";
 
   // alias = train, train_data, train_data_file, data_filename
@@ -598,22 +599,26 @@ struct Config {
   // desc = with quantized training, most arithmetics in the training process will be integer operations
   // desc = gradient quantization can accelerate training, with little accuracy drop in most cases
   // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = *New in version 4.0.0*
   bool use_quantized_grad = false;
 
   // [no-save]
   // desc = number of bins to quantization gradients and hessians
   // desc = with more bins, the quantized training will be closer to full precision training
   // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = *New in 4.0.0*
   int num_grad_quant_bins = 4;
 
   // [no-save]
   // desc = whether to renew the leaf values with original gradients when quantized training
   // desc = renewing is very helpful for good quantized training accuracy for ranking objectives
   // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = *New in 4.0.0*
   bool quant_train_renew_leaf = false;
 
   // [no-save]
   // desc = whether to use stochastic rounding in gradient quantization
+  // desc = *New in 4.0.0*
   bool stochastic_rounding = true;
 
   #ifndef __NVCC__
@@ -777,6 +782,7 @@ struct Config {
   // desc = path to a ``.json`` file that specifies customized parser initialized configuration
   // desc = see `lightgbm-transform <https://github.com/microsoft/lightgbm-transform>`__ for usage examples
   // desc = **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page <https://github.com/microsoft/lightgbm-transform/issues>`__
+  // desc = *New in 4.0.0*
   std::string parser_config_file = "";
 
   #ifndef __NVCC__