DiscourseSegmenter/setup.py at master · discourse-lab/DiscourseSegmenter · GitHub

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#!/usr/bin/env python

##################################################################
# Libraries
from distutils.core import setup
from os import path
import codecs
import glob

##################################################################
# Variables and Constants
pwd = path.abspath(path.dirname(__file__))
with codecs.open(path.join(pwd, "README.rst"), encoding="utf-8") as ifile:
    long_description = ifile.read()

##################################################################
# setup()
setup(
    name="dsegmenter",
    version="0.0.1.dev1",
    description=("Collection of discourse segmenters "
                 "(with pre-trained models for German)"),
    long_description=long_description,
    author="Wladimir Sidorenko (Uladzimir Sidarenka)",
    author_email="sidarenk@uni-potsdam.de",
    license="MIT",
    url="https://github.com/discourse-lab/DiscourseSegmenter",
    include_package_data=True,
    packages=["dsegmenter", "dsegmenter.bparseg", "dsegmenter.edseg",
              "dsegmenter.treeseg", "dsegmenter.mateseg",
              "dsegmenter.evaluation"],
    # package_dir = {"dsegmenter.bparseg": "dsegmenter",
    #                "dsegmenter.edseg": "dsegmenter",
    #                "dsegmenter.treeseg": "dsegmenter"},
    package_data={
        "dsegmenter.edseg": [path.join("data", fname) for fname in (
            "dass_verbs.txt", "discourse_preps.txt", "finite_verbs.txt",
            "reporting_verbs.txt", "skip_rules.txt")],
        "dsegmenter.bparseg": [path.join("data", "*.npy"),
                               path.join("data", "*.model")],
        "dsegmenter.mateseg": [path.join("data", "mate.model")]},
    requires=["numpy (>=1.9.2)",
              "scipy (>=0.16.0)",
              "nltk (>=3.0.2)",
              "scikit.learn (>=0.15.2)",
              "segeval (>=2.0.11)"],
    provides=["dsegmenter (0.0.1)"],
    scripts=[path.join("scripts", "discourse_segmenter"),
             path.join("scripts", "evaluation"),
             path.join("scripts", "mate_segmenter")],
    classifiers=["Development Status :: 2 - Pre-Alpha",
                 "Environment :: Console",
                 "Intended Audience :: Science/Research",
                 "License :: OSI Approved :: MIT License",
                 "Natural Language :: German",
                 "Operating System :: Unix",
                 "Operating System :: MacOS",
                 # "Operating System :: Microsoft :: Windows",
                 "Programming Language :: Python :: 2",
                 "Programming Language :: Python :: 2.6",
                 "Programming Language :: Python :: 2.7",
                 # "Programming Language :: Python :: 3",
                 "Topic :: Text Processing :: Linguistic"],
    keywords="discourse segmentation NLP linguistics")