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[docs] add versionadded notes for v4.0.0 features #5948
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@@ -233,6 +233,8 @@ You could edit your firewall rules to allow communication between any of the wor | |||
Using Custom Objective Functions with Dask | |||
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.. versionadded:: 4.0.0 |
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@@ -145,6 +145,8 @@ Core Parameters | |||
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- ``goss``, Gradient-based One-Side Sampling | |||
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- *New in 4.0.0* |
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@@ -670,6 +672,8 @@ Learning Control Parameters | |||
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- **Note**: can be used only with ``device_type = cpu`` | |||
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- *New in version 4.0.0* |
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@@ -678,6 +682,8 @@ Learning Control Parameters | |||
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- **Note**: can be used only with ``device_type = cpu`` | |||
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- *New in 4.0.0* |
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@@ -686,10 +692,14 @@ Learning Control Parameters | |||
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- **Note**: can be used only with ``device_type = cpu`` | |||
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- *New in 4.0.0* |
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- ``stochastic_rounding`` :raw-html:`<a id="stochastic_rounding" title="Permalink to this parameter" href="#stochastic_rounding">🔗︎</a>`, default = ``true``, type = bool | ||
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- whether to use stochastic rounding in gradient quantization | ||
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- *New in 4.0.0* |
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@@ -908,6 +918,8 @@ Dataset Parameters | |||
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- **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>`__ | |||
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- *New in 4.0.0* |
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@@ -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. | |||
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.. versionadded:: 4.0.0 |
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@@ -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. | |||
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.. versionadded:: 4.0.0 |
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@@ -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``. | |||
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.. versionadded:: 4.0.0 |
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weight : list, numpy 1-D array, pandas Series or None, optional (default=None) | ||
Weight for each ``data`` instance. Weights should be non-negative. | ||
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.. versionadded:: 4.0.0 |
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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. | ||
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.. versionadded:: 4.0.0 |
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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``. | ||
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.. versionadded:: 4.0.0 |
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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. | ||
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.. versionadded:: 4.0.0 |
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@@ -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. | |||
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.. versionadded:: 4.0.0 |
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dataset_params : dict or None, optional (default=None) | ||
Other parameters for Dataset ``data``. | ||
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.. versionadded:: 4.0.0 |
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free_raw_data : bool, optional (default=True) | ||
If True, raw data is freed after constructing inner Dataset for ``data``. | ||
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.. versionadded:: 4.0.0 |
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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. | ||
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.. versionadded:: 4.0.0 |
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@@ -4270,6 +4301,8 @@ def set_leaf_output( | |||
) -> 'Booster': | |||
"""Set the output of a leaf. | |||
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.. versionadded:: 4.0.0 |
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@@ -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. | |||
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# https://github.com/microsoft/LightGBM/pull/4580 | |||
.. versionadded:: 4.0.0 |
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@@ -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. | |||
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.. versionadded:: 4.0.0 |
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@@ -672,6 +675,8 @@ def create_tree_digraph( | |||
graph = lgb.create_tree_digraph(clf, max_category_values=5) | |||
HTML(graph._repr_image_svg_xml()) | |||
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.. versionadded:: 4.0.0 |
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@@ -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). | |||
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.. versionchanged:: 4.0.0 |
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@@ -968,6 +971,9 @@ def n_estimators_(self) -> int: | |||
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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``. | |||
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# https://github.com/microsoft/LightGBM/pull/4753 | |||
.. versionadded:: 4.0.0 |
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@@ -979,6 +984,9 @@ def n_iter_(self) -> int: | |||
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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``. | |||
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# https://github.com/microsoft/LightGBM/pull/4753 | |||
.. versionadded:: 4.0.0 |
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This pull request has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this. |
Contributes to #5153.
Adds notes to docs pointing out things that are new as of v4.0.0.
For the Python package, I'm proposing doing this via Sphinx
.. versionadded::
directives: https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#directive-versionadded.For the docs generated from
config.h
and for the R package, I added regular-old notes in Italics, but in exactly the same format as what.. versionadded::
does ("New in version 4.0.0").example of the R docs in RStudio (click me)
example of the Python API docs (click me)
example of the parameter docs (click me)
Notes for Reviewers
I've temporarily enabled this branch on readthedocs.