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setup.py
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setup.py
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import os
from setuptools import find_packages, setup
from nlaugmenter import __version__
from nlaugmenter.evaluation.TestRunner import OperationRuns
from nlaugmenter.utils.mapper import map_filter, map_transformation
NAME = "nlaugmenter"
def all_folders(search: str, transformation_type: str) -> list:
"""
Get all folder names for either the transformations or filters
Parameters:
-----------
search: str,
search term, can be either 'transformations' or 'filters'.
transformation_type: str,
if 'transformations' is the search term then specify what type is it (light or heavy).
Returns:
--------
list of folder names.
"""
folder_names = [
NAME + "/" + search + "/" + f
for f in list(
OperationRuns.get_all_folder_names(search, transformation_type)
)
]
return folder_names
def read(fname):
with open(os.path.join(os.path.dirname(__file__), fname)) as f:
data = f.read()
return data
def recursive_requirements(search: str, transformation_type: str) -> str:
# (1) read all requirements.txt in the folder.
requirements = "\n"
for folder in all_folders(search, transformation_type):
r_file = os.path.join(
os.path.dirname(__file__), folder + "/requirements.txt"
)
if os.path.isfile(r_file):
with open(r_file) as f:
requirements += f.read() + "\n"
return requirements
def get_default_requirements() -> list:
"""
Populate the default requirements to be installed for the library.
Returns:
-------
list
list of requirements.
"""
# Get the default requirements (light transformations and light filters)
# (1) read main requirements.txt
mandatory_requirements = read("requirements.txt")
# (2) read requirements for light transformations
mandatory_requirements += recursive_requirements(
"transformations", "light"
)
# (3) read requirements for light filters
mandatory_requirements += recursive_requirements(
"filters", "light"
) # light filters
return filter_requirements(mandatory_requirements)
def filter_requirements(requirements: str) -> list:
"""Filter the requirements, exclude comments, empty strings
Parameters:
-----------
requirements: str,
string of requirements
Returns:
--------
list
list of filtered requirements
"""
requirement_list = requirements.split("\n")
# Remove all comments and empty string.
requirement_list = set(
filter(lambda req: "#" not in req and req != "", requirement_list)
)
return list(requirement_list)
def get_extra_requirements() -> dict:
"""
Get the dict of requirements for all the heavy transformations and filters.
If a user specifies a heavy transformation or filter, the corresponding requirements
from the generated dictionary will be picked up and installed along with the default
requirements.
The generated dictionary will be of this format:
{
'lost_in_translation': ['rouge_score'],
'mr_value_replacement': ['torchtext==0.9.1'],
'ocr_perturbation': ['trdg==1.6.0', 'tesserocr>=2.5.2'],
'pinyin': ['g2pM==0.1.2.5'],
'punctuation': ['cucco==2.2.1', 'fastpunct==2.0.2'],
'sentence_reordering': ['allennlp==2.5.0', 'allennlp-models==2.5.0'],
'synonym_substitution': ['nltk==3.6.2'],
'token_replacement': ['editdistance>=0.5.3'],
'transformer_fill': ['torch', 'transformers', 'spacy'],
'toxicity': ['detoxify==0.2.2']
}
Example usage: pip install nl-augmenter[lost_in_translation]
Returns:
-------
dict
dict of requirements for all the heavy transformations and filters.
"""
# Dictionary of requirements
requirements = {}
# Heavy transformations picked from mapper.py
for entry in map_transformation["heavy"]:
file_name = NAME + "/transformations/" + entry + "/requirements.txt"
if os.path.exists(file_name):
req_string = read(file_name)
requirements[entry] = filter_requirements(req_string)
# Heavy filters picked from mapper.py
for entry in map_filter["heavy"]:
file_name = NAME + "/filters/" + entry + "/requirements.txt"
if os.path.exists(file_name):
req_string = read(file_name)
requirements[entry] = filter_requirements(req_string)
return requirements
setup(
name=NAME,
version=__version__,
description="NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation",
author_email="nl-augmenter@googlegroups.com",
long_description=read("README.md"),
long_description_content_type="text/markdown",
license="MIT",
url="https://github.com/GEM-benchmark/NL-Augmenter",
project_urls={
"Bug Tracker": "https://github.com/GEM-benchmark/NL-Augmenter/issues",
"Web Page": "https://gem-benchmark.com/nl_augmenter",
},
install_requires=get_default_requirements(),
extras_require=get_extra_requirements(),
keywords=[
"augmentation",
"natural language processing",
"NLP",
"filters",
"deep learning",
"text processing",
"machine learning",
],
classifiers=[
"License :: OSI Approved :: MIT License",
"Intended Audience :: Developers",
"Intended Audience :: Information Technology",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Operating System :: OS Independent",
],
packages=find_packages(exclude=["test", "docs"]),
package_data={
"": ["*.json", "*.txt", "*.tsv", "*.csv", "*.npz", "*.ckpt"]
},
include_package_data=True,
python_requires=">=3.7",
)