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Implement new save_raw in Python. (#7572)

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* Expose the new C API function to Python.
* Remove old document and helper script.
* Small optimization to the `save_raw` and Json ctors.
This commit is contained in:
Jiaming Yuan
2022-01-19 02:27:51 +08:00
committed by GitHub
parent 9f20a3315e
commit dac9eb13bd
8 changed files with 104 additions and 150 deletions
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doc/tutorials/saving_model.rst
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@@ -2,16 +2,18 @@
Introduction to Model IO
########################
In XGBoost 1.0.0, we introduced experimental support of using `JSON
In XGBoost 1.0.0, we introduced support of using `JSON
<https://www.json.org/json-en.html>`_ for saving/loading XGBoost models and related
hyper-parameters for training, aiming to replace the old binary internal format with an
open format that can be easily reused. The support for binary format will be continued in
the future until JSON format is no-longer experimental and has satisfying performance.
This tutorial aims to share some basic insights into the JSON serialisation method used in
XGBoost. Without explicitly mentioned, the following sections assume you are using the
JSON format, which can be enabled by providing the file name with ``.json`` as file
extension when saving/loading model: ``booster.save_model('model.json')``. More details
below.
open format that can be easily reused. Later in XGBoost 1.6.0, additional support for
`Universal Binary JSON <https://ubjson.org/>`__ is added as an optimization for more
efficient model IO. They have the same document structure with different representations,
and we will refer them collectively as the JSON format. This tutorial aims to share some
basic insights into the JSON serialisation method used in XGBoost. Without explicitly
mentioned, the following sections assume you are using the one of the 2 outputs formats,
which can be enabled by providing the file name with ``.json`` (or ``.ubj`` for binary
JSON) as file extension when saving/loading model: ``booster.save_model('model.json')``.
More details below.
Before we get started, XGBoost is a gradient boosting library with focus on tree model,
which means inside XGBoost, there are 2 distinct parts:
@@ -53,7 +55,8 @@ Other language bindings are still working in progress.
based serialisation methods.
To enable JSON format support for model IO (saving only the trees and objective), provide
a filename with ``.json`` as file extension:
a filename with ``.json`` or ``.ubj`` as file extension, the latter is the extension for
`Universal Binary JSON <https://ubjson.org/>`__
.. code-block:: python
:caption: Python
@@ -65,7 +68,7 @@ a filename with ``.json`` as file extension:
xgb.save(bst, 'model_file_name.json')
While for memory snapshot, JSON is the default starting with xgboost 1.3.
While for memory snapshot, UBJSON is the default starting with xgboost 1.6.
***************************************************************
A note on backward compatibility of models and memory snapshots
@@ -105,15 +108,10 @@ Loading pickled file from different version of XGBoost
As noted, pickled model is neither portable nor stable, but in some cases the pickled
models are valuable. One way to restore it in the future is to load it back with that
specific version of Python and XGBoost, export the model by calling `save_model`. To help
easing the mitigation, we created a simple script for converting pickled XGBoost 0.90
Scikit-Learn interface object to XGBoost 1.0.0 native model. Please note that the script
suits simple use cases, and it's advised not to use pickle when stability is needed. It's
located in ``xgboost/doc/python`` with the name ``convert_090to100.py``. See comments in
the script for more details.
specific version of Python and XGBoost, export the model by calling `save_model`.
A similar procedure may be used to recover the model persisted in an old RDS file. In R, you are
able to install an older version of XGBoost using the ``remotes`` package:
A similar procedure may be used to recover the model persisted in an old RDS file. In R,
you are able to install an older version of XGBoost using the ``remotes`` package:
.. code-block:: r
@@ -244,10 +242,3 @@ leaf directly, instead it saves the weights as a separated array.
.. include:: ../model.schema
:code: json
************
Future Plans
************
Right now using the JSON format incurs longer serialisation time, we have been working on
optimizing the JSON implementation to close the gap between binary format and JSON format.