Datasets:

google-research-datasets
/

newsgroup

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""20Newsgroup dataset"""

	from __future__ import absolute_import, division, print_function

	import os

	import datasets


	_CITATION = """
	@inproceedings{Lang95,
	author = {Ken Lang},
	title = {Newsweeder: Learning to filter netnews}
	year = {1995}
	booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}
	pages = {331-339}
	}
	"""

	_DESCRIPTION = """
	The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across
	20 different newsgroups. The 20 newsgroups collection has become a popular data set for experiments in text applications of
	machine learning techniques, such as text classification and text clustering.
	"""

	_DOWNLOAD_URL = {
	"bydate": "http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz",
	"19997": "http://qwone.com/~jason/20Newsgroups/20news-19997.tar.gz",
	"18828": "http://qwone.com/~jason/20Newsgroups/20news-18828.tar.gz",
	}
	_NEWS_GROUPS = [
	"comp.graphics",
	"comp.os.ms-windows.misc",
	"comp.sys.ibm.pc.hardware",
	"comp.sys.mac.hardware",
	"comp.windows.x",
	"rec.autos",
	"rec.motorcycles",
	"rec.sport.baseball",
	"rec.sport.hockey",
	"sci.crypt",
	"sci.electronics",
	"sci.med",
	"sci.space",
	"misc.forsale",
	"talk.politics.misc",
	"talk.politics.guns",
	"talk.politics.mideast",
	"talk.religion.misc",
	"alt.atheism",
	"soc.religion.christian",
	]
	_VERSIONS = {"19997": "1.0.0", "bydate": "2.0.0", "18828": "3.0.0"}

	_DESC = {
	"19997": "the original, unmodified version.",
	"bydate": "sorted by date into training(60%) and test(40%) sets, does not include cross-posts (duplicates) and does not include newsgroup-identifying headers (Xref, Newsgroups, Path, Followup-To, Date)",
	"18828": 'does not include cross-posts and includes only the "From" and "Subject" headers.',
	}
	_CONFIG_NAMES = []
	for version in _VERSIONS:
	for group in _NEWS_GROUPS:
	_CONFIG_NAMES.append(version + "_" + group)

	_CONFIG_NAMES = sorted(_CONFIG_NAMES)


	class NewsgroupConfig(datasets.BuilderConfig):
	"""BuilderConfig for 20Newsgroup."""

	def __init__(self, sub_dir, **kwargs):
	"""Constructs a 20Newsgroup.

	Args:
	sub_dirs: str
	**kwargs: keyword arguments forwarded to super.
	"""

	super(NewsgroupConfig, self).__init__(**kwargs)
	self.sub_dir = sub_dir


	class Newsgroups(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	NewsgroupConfig(
	name=name,
	description=_DESC[name.split("_")[0]],
	sub_dir=name.split("_")[1],
	version=datasets.Version(_VERSIONS[name.split("_")[0]]),
	)
	for name in _CONFIG_NAMES
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION + "\n" + self.config.description,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	}
	),
	homepage="http://qwone.com/~jason/20Newsgroups/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	url = _DOWNLOAD_URL[self.config.name.split("_")[0]]
	path = dl_manager.download_and_extract(url)
	if self.config.name.startswith("bydate"):

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files_path": os.path.join(path, "20news-bydate-train", self.config.sub_dir)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"files_path": os.path.join(path, "20news-bydate-train", self.config.sub_dir)},
	),
	]
	elif self.config.name.startswith("19997"):
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files_path": os.path.join(path, "20_newsgroups", self.config.sub_dir)},
	)
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files_path": os.path.join(path, "20news-18828", self.config.sub_dir)},
	)
	]

	def _generate_examples(self, files_path):
	"""Yields examples."""
	files = sorted(os.listdir(files_path))
	for id_, file in enumerate(files):
	filepath = os.path.join(files_path, file)
	with open(
	filepath, encoding="utf8", errors="ignore"
	) as f: # here we can ignore byte encoded tokens. we only have a very few and in most case it happens at the end of the file (kind of \FF)
	text = f.read()
	yield id_, {"text": text}