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scikit-learn/sklearn/ensemble/setup.py

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added gbrt extension module (faster prediction methods)
2012-03-29 14:34:45 +08:00
import numpy
from numpy.distutils.misc_util import Configuration
def configuration(parent_package="", top_path=None):
config = Configuration("ensemble", parent_package, top_path)
[MRG+2] Faster Gradient Boosting Decision Trees with binned features (#12807)
2019-04-27 03:14:59 +08:00
added gbrt extension module (faster prediction methods)
2012-03-29 14:34:45 +08:00
config.add_extension("_gradient_boosting",
[MRG+2] BUILD Rewrite setup.py files to handle cython dependencies (#7719) * Rewriting of cythonization in setup.py By using Cython.Build.cythonize and switching between .c and .pyx files as appropriate cython dependencies are correctly taken into account. * Use cythonize once on the root config rather than in each subpackage * Fix for Windows * Remove caching from Travis Cython dependencies are taken care of by Cython.Build.cythonize and based on file timestamps, so .C and .so files will always be rebuild from scratch on each build in Travis. * Specify .pyx in setup.files for cython generated extensions More natural this way. Tweak the extensions to generate from .c and .cpp files for a release. * COSMIT Remove commented out code * Check cython version is greater than 0.23 * COSMIT better names for functions * flake8 fix (imported module not at top of file) * Install cython 0.23 for Python 2.6 now that cython >= 0.23 requirement is enforced in setup.py * Use module constant for minimum required cython version * Fix Travis install.sh No easy way to put comments inside multi-line command
2016-11-02 19:57:04 +08:00
sources=["_gradient_boosting.pyx"],
added gbrt extension module (faster prediction methods)
2012-03-29 14:34:45 +08:00
include_dirs=[numpy.get_include()])
config.add_subpackage("tests")
[MRG+2] Faster Gradient Boosting Decision Trees with binned features (#12807)
2019-04-27 03:14:59 +08:00
# Histogram-based gradient boosting files
config.add_extension(
"_hist_gradient_boosting._gradient_boosting",
sources=["_hist_gradient_boosting/_gradient_boosting.pyx"],
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting.histogram",
sources=["_hist_gradient_boosting/histogram.pyx"],
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting.splitting",
sources=["_hist_gradient_boosting/splitting.pyx"],
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting._binning",
sources=["_hist_gradient_boosting/_binning.pyx"],
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting._predictor",
sources=["_hist_gradient_boosting/_predictor.pyx"],
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting._loss",
sources=["_hist_gradient_boosting/_loss.pyx"],
include_dirs=[numpy.get_include()])
ENH Native support for missing values in GBDTs (#13911) * Added NaN support in mapper * pep * WIP * some more * WIP * WIP * bug fix * basic tests * some doc * avoid some interactions * Added tag * better test * decent test + fix bug * add missing_fraction param to benchmark * bin training and validation data separately * shorter test * Map missing values to first bin instead of last * pep8 * Added whats new entry * avoid some python interactions * make predict_binned work * fixed bug due to offset in bin_thresholds_ attribute * more sensible binning strat * typo * user name * Add small test * convert to fortran array in tests * some doc * Added function test * pep8 * Bin validation data using binmaper of training data * Allocate first bin for missing entries based on the whole data, not just training data. * Addressed Thomas' comments * Update sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py * Addressed Guillaume's comments * always allocate first bin for missing values * reduce diff * minor more consistent test * typo * WIP * some doc * reduce diff * pep8 * minor * remove prints * towards nan only splits * don't check right to left on split_on_nan * cleaups * format and comment * Fixed bug + added more tests * refactor tests * put back n_threads to max value * minor changes * minor cleaning * Add (failing) test that checks equivalence with min max imputation * Decrease the likelihood of ties when training the trees * More robust test * Fix pytest parametrization * Check bin thresholds in test * Try to make the test even easier to see if the Linux 32bit build would pass in this case * Don't check last non-missing bin if there's no nan * Improve min-max imputation test * FIX: _find_best_bin_to_split_right_to_left is still required even when left to right wants to split on nans * comments * remove split_on_nan * ooops deleted useless files * Got rid of individual checks in predictor code +inf thresholds are only allowed in a split on nan situation. Thresholds that are computed as +inf are capped to a very high constant value * can also remove special case in binning code * minor typos + more consistent test * renamed types -> common * 1e300 -> almost inf * added user guide section on missing values * Addressed Olivier's comment + updated whatsnew * addressed comments * Fix doctest formatting * Fix nan predictive doctest
2019-08-21 17:22:00 +08:00
config.add_extension("_hist_gradient_boosting.common",
sources=["_hist_gradient_boosting/common.pyx"],
[MRG+2] Faster Gradient Boosting Decision Trees with binned features (#12807)
2019-04-27 03:14:59 +08:00
include_dirs=[numpy.get_include()])
config.add_extension("_hist_gradient_boosting.utils",
sources=["_hist_gradient_boosting/utils.pyx"],
include_dirs=[numpy.get_include()])
config.add_subpackage("_hist_gradient_boosting.tests")
added gbrt extension module (faster prediction methods)
2012-03-29 14:34:45 +08:00
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(**configuration().todict())