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setup.py
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setup.py
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#! /usr/bin/env python
#
# License: 3-clause BSD
import sys
import os
import platform
import shutil
from distutils.command.clean import clean as Clean
from pkg_resources import parse_version
import traceback
try:
import builtins
except ImportError:
# Python 2 compat: just to be able to declare that Python >=3.5 is needed.
import __builtin__ as builtins
# This is a bit (!) hackish: we are setting a global variable so that the
# main sklearn __init__ can detect if it is being loaded by the setup
# routine, to avoid attempting to load components that aren't built yet:
# the numpy distutils extensions that are used by scikit-learn to
# recursively build the compiled extensions in sub-packages is based on the
# Python import machinery.
builtins.__SKLEARN_SETUP__ = True
DISTNAME = 'oblique_forests'
DESCRIPTION = 'A set of python modules for machine learning and data mining'
with open('README.md') as f:
LONG_DESCRIPTION = f.read()
MAINTAINER = 'Adam Li, Chester Huynh, Parth Vora'
MAINTAINER_EMAIL = ''
URL = ''
DOWNLOAD_URL = ''
LICENSE = 'new BSD'
PROJECT_URLS = {}
# We can actually import a restricted version of sklearn that
# does not need the compiled code
import oblique_forests
VERSION = oblique_forests.__version__
if platform.python_implementation() == 'PyPy':
SCIPY_MIN_VERSION = '1.1.0'
NUMPY_MIN_VERSION = '1.14.0'
else:
SCIPY_MIN_VERSION = '0.17.0'
NUMPY_MIN_VERSION = '1.11.0'
JOBLIB_MIN_VERSION = '0.11'
# Optional setuptools features
# We need to import setuptools early, if we want setuptools features,
# as it monkey-patches the 'setup' function
# For some commands, use setuptools
SETUPTOOLS_COMMANDS = {
'develop', 'release', 'bdist_egg', 'bdist_rpm',
'bdist_wininst', 'install_egg_info', 'build_sphinx',
'egg_info', 'easy_install', 'upload', 'bdist_wheel',
'--single-version-externally-managed',
}
if SETUPTOOLS_COMMANDS.intersection(sys.argv):
import setuptools
extra_setuptools_args = dict(
zip_safe=False, # the package can run out of an .egg file
include_package_data=True,
extras_require={
'alldeps': (
'numpy >= {}'.format(NUMPY_MIN_VERSION),
'scipy >= {}'.format(SCIPY_MIN_VERSION),
),
},
)
else:
extra_setuptools_args = dict()
# Custom clean command to remove build artifacts
class CleanCommand(Clean): # noqa
description = "Remove build artifacts from the source tree"
def run(self): # noqa
Clean.run(self)
# Remove c files if we are not within a sdist package
cwd = os.path.abspath(os.path.dirname(__file__))
remove_c_files = not os.path.exists(os.path.join(cwd, 'PKG-INFO'))
if remove_c_files:
print('Will remove generated .c files')
if os.path.exists('build'):
shutil.rmtree('build')
for dirpath, dirnames, filenames in os.walk('oblique_forests'):
for filename in filenames:
if any(filename.endswith(suffix) for suffix in
(".so", ".pyd", ".dll", ".pyc")):
os.unlink(os.path.join(dirpath, filename))
continue
extension = os.path.splitext(filename)[1]
if remove_c_files and extension in ['.c', '.cpp']:
pyx_file = str.replace(filename, extension, '.pyx')
if os.path.exists(os.path.join(dirpath, pyx_file)):
os.unlink(os.path.join(dirpath, filename))
for dirname in dirnames:
if dirname == '__pycache__':
shutil.rmtree(os.path.join(dirpath, dirname))
cmdclass = {'clean': CleanCommand}
# custom build_ext command to set OpenMP compile flags depending on os and
# compiler
# build_ext has to be imported after setuptools
try:
from numpy.distutils.command.build_ext import build_ext # noqa
class build_ext_subclass(build_ext): # noqa
def build_extensions(self): # noqa
from oblique_forests._build_utils.openmp_helpers import get_openmp_flag
if not os.getenv('SKLEARN_NO_OPENMP'):
openmp_flag = get_openmp_flag(self.compiler)
for e in self.extensions:
e.extra_compile_args += openmp_flag
e.extra_link_args += openmp_flag
build_ext.build_extensions(self)
cmdclass['build_ext'] = build_ext_subclass
except ImportError:
# Numpy should not be a dependency just to be able to introspect
# that python 3.5 is required.
pass
# Optional wheelhouse-uploader features
# To automate release of binary packages for scikit-learn we need a tool
# to download the packages generated by travis and appveyor workers (with
# version number matching the current release) and upload them all at once
# to PyPI at release time.
# The URL of the artifact repositories are configured in the setup.cfg file.
WHEELHOUSE_UPLOADER_COMMANDS = {'fetch_artifacts', 'upload_all'}
if WHEELHOUSE_UPLOADER_COMMANDS.intersection(sys.argv):
import wheelhouse_uploader.cmd
cmdclass.update(vars(wheelhouse_uploader.cmd))
def configuration(parent_package='', top_path=None): # noqa
if os.path.exists('MANIFEST'):
os.remove('MANIFEST')
from numpy.distutils.misc_util import Configuration
config = Configuration(None, parent_package, top_path)
# Avoid non-useful msg:
# "Ignoring attempt to set 'name' (from ... "
config.set_options(ignore_setup_xxx_py=True,
assume_default_configuration=True,
delegate_options_to_subpackages=True,
quiet=True)
config.add_subpackage('oblique_forests')
return config
def get_numpy_status(): # noqa
"""
Return a dictionary containing a boolean specifying whether NumPy
is up-to-date, along with the version string (empty string if
not installed).
"""
numpy_status = {}
try:
import numpy
numpy_version = numpy.__version__
numpy_status['up_to_date'] = parse_version(
numpy_version) >= parse_version(NUMPY_MIN_VERSION)
numpy_status['version'] = numpy_version
except ImportError:
traceback.print_exc()
numpy_status['up_to_date'] = False
numpy_status['version'] = ""
return numpy_status
def setup_package(): # noqa
metadata = dict(name=DISTNAME,
maintainer=MAINTAINER,
maintainer_email=MAINTAINER_EMAIL,
description=DESCRIPTION,
license=LICENSE,
url=URL,
download_url=DOWNLOAD_URL,
project_urls=PROJECT_URLS,
version=VERSION,
long_description=LONG_DESCRIPTION,
classifiers=['Intended Audience :: Science/Research',
'Intended Audience :: Developers',
'License :: OSI Approved',
'Programming Language :: C',
'Programming Language :: Python',
'Topic :: Software Development',
'Topic :: Scientific/Engineering',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Operating System :: Unix',
'Operating System :: MacOS',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
('Programming Language :: Python :: '
'Implementation :: CPython'),
('Programming Language :: Python :: '
'Implementation :: PyPy')
],
cmdclass=cmdclass,
python_requires=">=3.5",
install_requires=[
'numpy>={}'.format(NUMPY_MIN_VERSION),
'scipy>={}'.format(SCIPY_MIN_VERSION),
'joblib>={}'.format(JOBLIB_MIN_VERSION)
],
**extra_setuptools_args)
if len(sys.argv) == 1 or (
len(sys.argv) >= 2 and ('--help' in sys.argv[1:] or
sys.argv[1] in ('--help-commands',
'egg_info',
'--version',
'clean'))):
# For these actions, NumPy is not required
#
# They are required to succeed without Numpy for example when
# pip is used to install Scikit-learn when Numpy is not yet present in
# the system.
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
metadata['version'] = VERSION
else:
if sys.version_info < (3, 5):
raise RuntimeError(
"Scikit-learn requires Python 3.5 or later. The current"
" Python version is %s installed in %s."
% (platform.python_version(), sys.executable))
numpy_status = get_numpy_status()
numpy_req_str = "scikit-learn requires NumPy >= {}.\n".format(
NUMPY_MIN_VERSION)
instructions = ("Installation instructions are available on the "
"scikit-learn website: "
"http://scikit-learn.org/stable/install.html\n")
if numpy_status['up_to_date'] is False:
if numpy_status['version']:
raise ImportError("Your installation of Numerical Python "
"(NumPy) {} is out-of-date.\n{}{}"
.format(numpy_status['version'],
numpy_req_str, instructions))
else:
raise ImportError("Numerical Python (NumPy) is not "
"installed.\n{}{}"
.format(numpy_req_str, instructions))
from numpy.distutils.core import setup
metadata['configuration'] = configuration
setup(**metadata)
if __name__ == "__main__":
setup_package()