[docs] add versionadded notes for v4.0.0 features

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__

python-package/lightgbm/basic.py

@@ -931,6 +931,8 @@ def predict(
             If True, ensure that the features used to predict match the ones used to train.
             Used only if data is pandas DataFrame.
 
+            .. versionadded:: 4.0.0
+
         Returns
         -------
         result : numpy array, scipy.sparse or list of scipy.sparse
@@ -2840,6 +2842,8 @@ def num_feature(self) -> int:
     def feature_num_bin(self, feature: Union[int, str]) -> int:
         """Get the number of bins for a feature.
 
+        .. versionadded:: 4.0.0
+
         Parameters
         ----------
         feature : int or str
@@ -4149,19 +4153,34 @@ def refit(
             will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees.
         reference : Dataset or None, optional (default=None)
             Reference for ``data``.
+
+            .. versionadded:: 4.0.0
+
         weight : list, numpy 1-D array, pandas Series or None, optional (default=None)
             Weight for each ``data`` instance. Weights should be non-negative.
+
+            .. versionadded:: 4.0.0
+
         group : list, numpy 1-D array, pandas Series or None, optional (default=None)
             Group/query size for ``data``.
             Only used in the learning-to-rank task.
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
+
+            .. versionadded:: 4.0.0
+
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for ``data``.
+
+            .. versionadded:: 4.0.0
+
         feature_name : list of str, or 'auto', optional (default="auto")
             Feature names for ``data``.
             If 'auto' and data is pandas DataFrame, data columns names are used.
+
+            .. versionadded:: 4.0.0
+
         categorical_feature : list of str or int, or 'auto', optional (default="auto")
             Categorical features for ``data``.
             If list of int, interpreted as indices.
@@ -4172,13 +4191,25 @@ def refit(
             All negative values in categorical features will be treated as missing values.
             The output cannot be monotonically constrained with respect to a categorical feature.
             Floating point numbers in categorical features will be rounded towards 0.
+
+            .. versionadded:: 4.0.0
+
         dataset_params : dict or None, optional (default=None)
             Other parameters for Dataset ``data``.
+
+            .. versionadded:: 4.0.0
+
         free_raw_data : bool, optional (default=True)
             If True, raw data is freed after constructing inner Dataset for ``data``.
+
+            .. versionadded:: 4.0.0
+
         validate_features : bool, optional (default=False)
             If True, ensure that the features used to refit the model match the original ones.
             Used only if data is pandas DataFrame.
+
+            .. versionadded:: 4.0.0
+
         **kwargs
             Other parameters for refit.
             These parameters will be passed to ``predict`` method.
@@ -4270,6 +4301,8 @@ def set_leaf_output(
     ) -> 'Booster':
         """Set the output of a leaf.
 
+        .. versionadded:: 4.0.0
+
         Parameters
         ----------
         tree_id : int

python-package/lightgbm/callback.py

@@ -407,6 +407,9 @@ def early_stopping(stopping_rounds: int, first_metric_only: bool = False, verbos
         If float, this single value is used for all metrics.
         If list, its length should match the total number of metrics.
 
+        # https://github.com/microsoft/LightGBM/pull/4580
+        .. versionadded:: 4.0.0
+
     Returns
     -------
     callback : _EarlyStoppingCallback

python-package/lightgbm/plotting.py

@@ -656,6 +656,9 @@ def create_tree_digraph(
     example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None)
         Single row with the same structure as the training data.
         If not None, the plot will highlight the path that sample takes through the tree.
+
+        .. versionadded:: 4.0.0
+
     max_category_values : int, optional (default=10)
         The maximum number of category values to display in tree nodes, if the number of thresholds is greater than this value, thresholds will be collapsed and displayed on the label tooltip instead.
 
@@ -672,6 +675,8 @@ def create_tree_digraph(
                 graph = lgb.create_tree_digraph(clf, max_category_values=5)
                 HTML(graph._repr_image_svg_xml())
 
+        .. versionadded:: 4.0.0
+
     **kwargs
         Other parameters passed to ``Digraph`` constructor.
         Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters.

python-package/lightgbm/sklearn.py

@@ -484,6 +484,9 @@ def __init__(
             threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds
             to using the number of physical cores in the system (its correct detection requires
             either the ``joblib`` or the ``psutil`` util libraries to be installed).
+
+            .. versionchanged:: 4.0.0
+
         importance_type : str, optional (default='split')
             The type of feature importance to be filled into ``feature_importances_``.
             If 'split', result contains numbers of times the feature is used in a model.
@@ -968,6 +971,9 @@ def n_estimators_(self) -> int:
 
         This might be less than parameter ``n_estimators`` if early stopping was enabled or
         if boosting stopped early due to limits on complexity like ``min_gain_to_split``.
+
+        # https://github.com/microsoft/LightGBM/pull/4753
+        .. versionadded:: 4.0.0
         """
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No n_estimators found. Need to call fit beforehand.')
@@ -979,6 +985,9 @@ def n_iter_(self) -> int:
 
         This might be less than parameter ``n_estimators`` if early stopping was enabled or
         if boosting stopped early due to limits on complexity like ``min_gain_to_split``.
+
+        # https://github.com/microsoft/LightGBM/pull/4753
+        .. versionadded:: 4.0.0
         """
         if not self.__sklearn_is_fitted__():
             raise LGBMNotFittedError('No n_iter found. Need to call fit beforehand.')