https://github.com/antoinecarme/pyaf
Revision 2d8810a60b27f77f9adb9272c3e9f502ca9d3eb0 authored by Antoine Carme on 19 April 2022, 17:06:00 UTC, committed by Antoine Carme on 19 April 2022, 17:06:00 UTC
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Tip revision: 2d8810a60b27f77f9adb9272c3e9f502ca9d3eb0 authored by Antoine Carme on 19 April 2022, 17:06:00 UTC
Investigate PyTorch-based LSTM and MLP models #199. Pytorch impact on model options.
Tip revision: 2d8810a
setup.py
from setuptools import setup
from setuptools import find_packages

with open("README.md", "r") as fh:
    pyaf_long_description = fh.read()
    
setup(name='pyaf',
      version='3.0',
      description='Python Automatic Forecasting',
      long_description=pyaf_long_description,
      long_description_content_type="text/markdown",
      author='Antoine CARME',
      author_email='antoine.carme@laposte.net',
      url='https://github.com/antoinecarme/pyaf',
      license='BSD 3-clause',
      packages=find_packages(include=['pyaf', 'pyaf.*']),
      python_requires='>=3',
      classifiers=['Development Status :: 5 - Production/Stable',
                   'Programming Language :: Python :: 3'],
      keywords='arx automatic-forecasting autoregressive benchmark cycle decomposition exogenous forecasting heroku hierarchical-forecasting horizon jupyter pandas python scikit-learn seasonal time-series transformation trend web-service',
      install_requires=[
          'scipy',
          'pandas',
          'sklearn',
          'matplotlib',
          'pydot',
          'dill',
          'sqlalchemy'
      ])
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