Currently xgboost can only be installed by running:
python setup.py install
Now it can be packaged (in binary form) as a wheel and installed like:
pip install xgboost-0.6-py2-none-any.whl
distutils and wheel install `data_files` differently than setuptools.
setuptools will install the `data_files` in the package directory whereas the
others install it in `sys.prefix`. By adding `sys.prefix` to the list of
directories to check for the shared library, xgboost can now be distributed as
a wheel.
* Fixed OpenMP installation on MacOSX with gcc-6
- Modified makefile from gcc-5 to gcc-6
- Removed deprecated install instructions from doc (gcc-5 was automatically forced if available in makefile on OSX)
* Fixed OpenMP installation on MacOSX with gcc-6
- Modified makefile from gcc-5 to gcc-6
- Removed deprecated install instructions from doc (gcc-5 was automatically forced if available in makefile on OSX)
make math better, specifically, unify the notation for Theta or theta. changed basic linear model notation from weight w to theta to make more consistent. Changed Obj function notation also
* force gcc-5 or clang-omp for Mac OS, prepare for pip pack
* add sklearn dep, make -j4
* finalize PyPI submission
* revert to Xcode clang for passing build #1468
* force to clang, try to solve cmake travis error
* remove sklearn dependency
* [R] do not remove zero coefficients from gblinear dump
* [R] switch from stringr to stringi
* fix#1399
* [R] separate ggplot backend, add base r graphics, cleanup, more plots, tests
* add missing include in amalgamation - fixes building R package in linux
* add forgotten file
* [R] fix DESCRIPTION
* [R] fix travis check issue and some cleanup
* Add deviance metric for gamma regression
* Simplify the computation of nloglik for gamma regression
* Add a description for gamma-deviance
* Minor fix
* Add support for Gamma regression
* Use base_score to replace the lp_bias
* Remove the lp_bias config block
* Add a demo for running gamma regression in Python
* Typo fix
* Revise the description for objective
* Add a script to generate the autoclaims dataset
