e960a09ff4
Changed the name of eval_results to evals_result, so that the naming is the same in training.py and sklearn.py
Made the structure of evals_result the same as in training.py, the names of the keys are different:
In sklearn.py you cannot name your evals_result, but they are automatically called 'validation_0', 'validation_1' etc.
The dict evals_result will output something like: {'validation_0': {'logloss': ['0.674800', '0.657121']}, 'validation_1': {'logloss': ['0.63776', '0.58372']}}
In training.py you can name your multiple evals_result with a watchlist like: watchlist = [(dtest,'eval'), (dtrain,'train')]
The dict evals_result will output something like: {'train': {'logloss': ['0.68495', '0.67691']}, 'eval': {'logloss': ['0.684877', '0.676767']}}
You can access the evals_result using the evals_result() function.
XGBoost Python Package
Installation
We are on PyPI now. For stable version, please install using pip:
pip install xgboost- Note for windows users: this pip installation may not work on some windows environment, and it may cause unexpected errors. pip installation on windows is currently disabled for further invesigation, please install from github.
For up-to-date version, please install from github.
- To make the python module, type
./build.shin the root directory of project - Make sure you have setuptools
- Install with
python setup.py installfrom this directory. - For windows users, please use the Visual Studio project file under windows folder. See also the installation tutorial from Kaggle Otto Forum.
Examples
- Refer also to the walk through example in demo folder
- See also the example scripts for Kaggle Higgs Challenge, including speedtest script on this dataset.
Note
- If you want to build xgboost on Mac OS X with multiprocessing support where clang in XCode by default doesn't support, please install gcc 4.9 or higher using homebrew
brew tap homebrew/versions; brew install gcc49 - If you want to run XGBoost process in parallel using the fork backend for joblib/multiprocessing, you must build XGBoost without support for OpenMP by
make no_omp=1. Otherwise, use the forkserver (in Python 3.4) or spawn backend. See the sklearn_parallel.py demo.