From 263b7befde373800a8f59fe16c456d5c0bdb39aa Mon Sep 17 00:00:00 2001 From: tqchen Date: Mon, 11 Jan 2016 16:18:54 -0800 Subject: [PATCH] [LOG] Simplfy README.md add change logs. --- CHANGES.md | 25 ++++++++++++++++++++++--- README.md | 39 +++++++++++---------------------------- 2 files changed, 33 insertions(+), 31 deletions(-) diff --git a/CHANGES.md b/CHANGES.md index 1a10f04e7..441ab8461 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -35,6 +35,7 @@ xgboost-0.4 * sklearn wrapper is supported in python module * Experimental External memory version + xgboost-0.47 ------------ * Changes in R library @@ -52,10 +53,28 @@ xgboost-0.47 - improved compatibility in sklearn module. - additional parameters added for sklearn wrapper. - added pip installation functionality. - - supports more Pandas DataFrame dtypes. + - supports more Pandas DataFrame dtypes. - added best_ntree_limit attribute, in addition to best_score and best_iteration. * Java api is ready for use * Added more test cases and continuous integration to make each build more robust. -on going at master ------------------- +xgboost brick: next release candidate +------------------------------------- +* Major refactor of core library. + - Goal: more flexible and modular code as a portable library. + - Switch to use of c++11 standard code. + - Random number generator defaults to ```std::mt19937```. + - Share the data loading pipeline and logging module from dmlc-core. + - Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader. + - Future plugin modules can be put into xgboost/plugin and register back to the library. + - Remove most of the raw pointers to smart ptrs, for RAII safety. +* Change library name to libxgboost.so +* Backward compatiblity + - The binary buffer file is not backward compatible with previous version. + - The model file is backward compatible on 64 bit platforms. +* The model file is compatible between 64/32 bit platforms(not yet tested). +* External memory version and other advanced features will be exposed to R library as well on linux. + - Previously some of the features are blocked due to C++11 and threading limits. + - The windows version is still blocked due to Rtools do not support ```std::thread```. +* rabit and dmlc-core are maintained through git submodule + - Anyone can open PR to update these dependencies now. diff --git a/README.md b/README.md index cbcbe3a6e..0586abcae 100644 --- a/README.md +++ b/README.md @@ -7,47 +7,31 @@ [![PyPI version](https://badge.fury.io/py/xgboost.svg)](https://pypi.python.org/pypi/xgboost/) [![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. - -It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data - -XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/) projects +XGBoost is an optimized distributed gradient boosting library designed to be highly *efficient*, *flexible* and *portable*. +It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework. +XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. +The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples. +XGBoost is part of [DMLC](http://dmlc.github.io/) projects. Contents -------- -* [What's New](#whats-new) -* [Version](#version) -* [Documentation](doc/index.md) -* [Build Instruction](doc/build.md) -* [Features](#features) -* [Distributed XGBoost](multi-node) +* [Documentation](https://xgboost.readthedocs.org) * [Usecases](doc/index.md#highlight-links) -* [Bug Reporting](#bug-reporting) -* [Contributing to XGBoost](#contributing-to-xgboost) +* [Code Examples](demo) +* [Build Instruction](doc/build.md) * [Committers and Contributors](CONTRIBUTORS.md) -* [License](#license) -* [XGBoost in Graphlab Create](#xgboost-in-graphlab-create) What's New ---------- - +* XGBoost [brick](CHANGES.md) * XGBoost helps Vlad Mironov, Alexander Guschin to win the [CERN LHCb experiment Flavour of Physics competition](https://www.kaggle.com/c/flavours-of-physics). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/11/30/flavour-of-physics-technical-write-up-1st-place-go-polar-bears/). * XGBoost helps Mario Filho, Josef Feigl, Lucas, Gilberto to win the [Caterpillar Tube Pricing competition](https://www.kaggle.com/c/caterpillar-tube-pricing). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/). * XGBoost helps Halla Yang to win the [Recruit Coupon Purchase Prediction Challenge](https://www.kaggle.com/c/coupon-purchase-prediction). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/). -* XGBoost helps Owen Zhang to win the [Avito Context Ad Click competition](https://www.kaggle.com/c/avito-context-ad-clicks). Check out the [interview from Kaggle](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/). -* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) - Check out the [winning solution](https://github.com/ChenglongChen/Kaggle_CrowdFlower) -* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) -* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) - Check out the [winning solution](doc/README.md#highlight-links) -* [External Memory Version](doc/external_memory.md) Version ------- - -* Current version xgboost-0.4 - - [Change log](CHANGES.md) - - This version is compatible with 0.3x versions +* Current version xgboost-0.6 (brick) + - See [Change log](CHANGES.md) for details Features -------- @@ -76,4 +60,3 @@ XGBoost has been developed and used by a group of active community members. Ever License ------- © Contributors, 2015. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license. -