* bump up to scala 2.11
* framework of data frame integration
* test consistency between RDD and DataFrame
* order preservation
* test order preservation
* example code and fix makefile
* improve type checking
* improve APIs
* user docs
* work around travis CI's limitation on log length
* adjust test structure
* integrate with Spark -1 .x
* spark 2.x integration
* remove spark 1.x implementation but provide instructions on how to downgrade
* [TREE] Experimental version of monotone constraint
* Allow default detection of montone option
* loose the condition of strict check
* Update gbtree.cc
Fixed to work with future versions of visual studio i.e., 2015
MSVC has it's own section for setting compile parameters, it shouldn't need to fall into section below i.e., checking for c++11 as this is definitely already supported, though this isn't an issue for Visual Studio 2012, it breaks for later versions
of visual studio i.e., 2015 when the default c++ is version 14. Though still backward compatible with c++11
* test consistency of prediction functions between DMatrix and RDD
* remove APIs with DMatrix from xgboost-spark
* fix compilation error in xgboost4j-example
* fix test cases
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
