a375ad2822
* [R] make sure things work for a single split model; fixes #2191 * [R] add option use_int_id to xgb.model.dt.tree * [R] add example of exporting tree plot to a file * [R] set save_period = NULL as default in xgboost() to be the same as in xgb.train; fixes #2182 * [R] it's a good practice after CRAN releases to bump up package version in dev * [R] allow xgb.DMatrix construction from integer dense matrices * [R] xgb.DMatrix: silent parameter; improve documentation * [R] xgb.model.dt.tree code style changes * [R] update NEWS with parameter changes * [R] code safety & style; handle non-strict matrix and inherited classes of input and model; fixes #2242 * [R] change to x.y.z.p R-package versioning scheme and set version to 0.6.4.3 * [R] add an R package versioning section to the contributors guide * [R] R-package/README.md: clean up the redundant old installation instructions, link the contributors guide
120 lines
5.0 KiB
Markdown
120 lines
5.0 KiB
Markdown
XGBoost Change Log
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==================
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This file records the changes in xgboost library in reverse chronological order.
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## in progress version
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* Refactored gbm to allow more friendly cache strategy
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- Specialized some prediction routine
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* Automatically remove nan from input data when it is sparse.
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- This can solve some of user reported problem of istart != hist.size
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* Minor fixes
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- Thread local variable is upgraded so it is automatically freed at thread exit.
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* Migrate to C++11
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- The current master version now requires C++11 enabled compiled(g++4.8 or higher)
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* R package:
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- New parameters:
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- `silent` in `xgb.DMatrix()`
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- `use_int_id` in `xgb.model.dt.tree()`
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- Default value of the `save_period` parameter in `xgboost()` changed to NULL (consistent with `xgb.train()`).
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## v0.6 (2016.07.29)
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* Version 0.5 is skipped due to major improvements in the core
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* Major refactor of core library.
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- Goal: more flexible and modular code as a portable library.
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- Switch to use of c++11 standard code.
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- Random number generator defaults to ```std::mt19937```.
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- Share the data loading pipeline and logging module from dmlc-core.
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- Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
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- Future plugin modules can be put into xgboost/plugin and register back to the library.
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- Remove most of the raw pointers to smart ptrs, for RAII safety.
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* Add official option to approximate algorithm `tree_method` to parameter.
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- Change default behavior to switch to prefer faster algorithm.
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- User will get a message when approximate algorithm is chosen.
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* Change library name to libxgboost.so
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* Backward compatiblity
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- The binary buffer file is not backward compatible with previous version.
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- The model file is backward compatible on 64 bit platforms.
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* The model file is compatible between 64/32 bit platforms(not yet tested).
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* External memory version and other advanced features will be exposed to R library as well on linux.
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- Previously some of the features are blocked due to C++11 and threading limits.
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- The windows version is still blocked due to Rtools do not support ```std::thread```.
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* rabit and dmlc-core are maintained through git submodule
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- Anyone can open PR to update these dependencies now.
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* Improvements
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- Rabit and xgboost libs are not thread-safe and use thread local PRNGs
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- This could fix some of the previous problem which runs xgboost on multiple threads.
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* JVM Package
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- Enable xgboost4j for java and scala
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- XGBoost distributed now runs on Flink and Spark.
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* Support model attributes listing for meta data.
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- https://github.com/dmlc/xgboost/pull/1198
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- https://github.com/dmlc/xgboost/pull/1166
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* Support callback API
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- https://github.com/dmlc/xgboost/issues/892
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- https://github.com/dmlc/xgboost/pull/1211
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- https://github.com/dmlc/xgboost/pull/1264
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* Support new booster DART(dropout in tree boosting)
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- https://github.com/dmlc/xgboost/pull/1220
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* Add CMake build system
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- https://github.com/dmlc/xgboost/pull/1314
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## v0.47 (2016.01.14)
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* Changes in R library
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- fixed possible problem of poisson regression.
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- switched from 0 to NA for missing values.
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- exposed access to additional model parameters.
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* Changes in Python library
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- throws exception instead of crash terminal when a parameter error happens.
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- has importance plot and tree plot functions.
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- accepts different learning rates for each boosting round.
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- allows model training continuation from previously saved model.
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- allows early stopping in CV.
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- allows feval to return a list of tuples.
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- allows eval_metric to handle additional format.
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- improved compatibility in sklearn module.
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- additional parameters added for sklearn wrapper.
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- added pip installation functionality.
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- supports more Pandas DataFrame dtypes.
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- added best_ntree_limit attribute, in addition to best_score and best_iteration.
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* Java api is ready for use
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* Added more test cases and continuous integration to make each build more robust.
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## v0.4 (2015.05.11)
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* Distributed version of xgboost that runs on YARN, scales to billions of examples
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* Direct save/load data and model from/to S3 and HDFS
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* Feature importance visualization in R module, by Michael Benesty
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* Predict leaf index
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* Poisson regression for counts data
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* Early stopping option in training
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* Native save load support in R and python
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- xgboost models now can be saved using save/load in R
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- xgboost python model is now pickable
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* sklearn wrapper is supported in python module
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* Experimental External memory version
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## v0.3 (2014.09.07)
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* Faster tree construction module
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- Allows subsample columns during tree construction via ```bst:col_samplebytree=ratio```
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* Support for boosting from initial predictions
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* Experimental version of LambdaRank
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* Linear booster is now parallelized, using parallel coordinated descent.
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* Add [Code Guide](src/README.md) for customizing objective function and evaluation
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* Add R module
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## v0.2x (2014.05.20)
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* Python module
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* Weighted samples instances
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* Initial version of pairwise rank
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## v0.1 (2014.03.26)
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* Initial release
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