cleanlab--cleanlab
Curtis G. Northcutt
79 行
2.8 KiB
Python
79 行
2.8 KiB
Python
# Written by Curtis G. Northcutt
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# For pypi upload
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# 0. python setup.py check -r -s
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# 0. rm -rf dist build
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# 1. python setup.py sdist bdist_wheel --universal
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# 2. python3 -m twine upload dist/*
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from setuptools import setup, find_packages
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# To use a consistent encoding
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from codecs import open
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from os import path
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here = path.abspath(path.dirname(__file__))
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# Get the long description from the README file
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with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
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long_description = f.read()
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# Get version number
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exec(open('cleanlab/version.py').read())
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setup(
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name='cleanlab',
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version=__version__,
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license='MIT',
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long_description=long_description,
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long_description_content_type='text/x-rst',
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description = 'The Python package for cleaning and learning with noisy labels. Works for all noisy label distributions, datasets, and models.',
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url = 'https://github.com/cgnorthcutt/cleanlab',
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author = 'Curtis G. Northcutt',
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author_email = 'cgn@mit.edu',
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# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
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classifiers=[
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'Development Status :: 3 - Alpha',
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'Intended Audience :: Developers',
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'Intended Audience :: Education',
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'Intended Audience :: Science/Research',
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'License :: OSI Approved :: MIT License',
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# We believe this package works will all versions, but we do not guarantee it!
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'Programming Language :: Python :: 2.7',
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'Programming Language :: Python :: 3.4',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python',
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'Topic :: Software Development',
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'Topic :: Scientific/Engineering',
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'Topic :: Scientific/Engineering',
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'Topic :: Scientific/Engineering :: Mathematics',
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'Topic :: Scientific/Engineering :: Artificial Intelligence',
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'Topic :: Software Development',
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'Topic :: Software Development :: Libraries',
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'Topic :: Software Development :: Libraries :: Python Modules',
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'Operating System :: Microsoft :: Windows',
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'Operating System :: POSIX',
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'Operating System :: Unix',
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'Operating System :: MacOS',
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],
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# What does your project relate to?
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keywords='machine_learning denoising classification weak_supervision learning_with_noisy_labels unsupervised_learning',
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# You can just specify the packages manually here if your project is
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# simple. Or you can use find_packages().
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packages=find_packages(exclude=['img', 'examples']),
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# List run-time dependencies here. These will be installed by pip when
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# your project is installed. For an analysis of "install_requires" vs pip's
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# requirements files see:
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# https://packaging.python.org/en/latest/requirements.html
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install_requires=['numpy>=1.11.3', 'scikit-learn>=0.18', 'scipy>=1.1.0', ],
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)
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