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setup.py
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setup.py
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from setuptools import setup, find_packages
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
try:
# Try to format our PyPi page as rst so it displays properly
import pypandoc
with open ('README.md', 'rb') as read_file:
readme_text = read_file.readlines()
# Change our README for pypi so we can get analytics tracking information for that separately
readme_text = [row.decode() for row in readme_text]
readme_text[-1] = "[![Analytics](https://ga-beacon.appspot.com/UA-58170643-5/concordia/pypi)](https://github.com/igrigorik/ga-beacon)"
long_description = pypandoc.convert(''.join(readme_text), 'rst', format='md')
except ImportError:
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('pypandoc (and possibly pandoc) are not installed. This means the PyPi package info will be formatted as .md instead of .rst. If you are encountering this before uploading a PyPi distribution, please install these')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='concordia',
version=open("concordia/_version.py").readlines()[-1].split()[-1].strip("\"'"),
description='Automated monitoring of machine learning models in production. Tracks and finds discrepancies in features, predictions, and labels',
long_description=long_description,
url='https://github.com/ClimbsRocks/Concordia',
author='Preston Parry',
author_email='ClimbsBytes@gmail.com',
license='MIT',
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'Intended Audience :: Information Technology',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: Scientific/Engineering :: Information Analysis',
'Topic :: Software Development :: Libraries :: Python Modules',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
keywords=['machine learning', 'data science', 'automated machine learning', 'deploying', 'machine learning in production', 'productionizing machine learning', 'tracking', 'feature discrepancies', 'train/serve skew', 'train serve skew', 'train-serve skew', 'model accuracy', 'alerts', 'monitoring', 'production ready', 'test coverage'],
packages=['concordia'],
install_requires=[
'auto_ml>=2.9.4',
'dill>=0.2.3, <0.3',
'pymongo>3.0, <4.0',
'redis>2.0, <3.0'
],
test_suite='nose.collector',
tests_require=['nose', 'coveralls']
)