Datasets:
guardian_authorship

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""This is an authorship attribution dataset based on the work of Stamatatos 2013. """


	import os

	import datasets


	_CITATION = """\
	@article{article,
	author = {Stamatatos, Efstathios},
	year = {2013},
	month = {01},
	pages = {421-439},
	title = {On the robustness of authorship attribution based on character n-gram features},
	volume = {21},
	journal = {Journal of Law and Policy}
	}

	@inproceedings{stamatatos2017authorship,
	title={Authorship attribution using text distortion},
	author={Stamatatos, Efstathios},
	booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
	volume={1}
	pages={1138--1149},
	year={2017}
	}
	"""

	_DESCRIPTION = """\
	A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
	1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
	2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

	3- The same-topic/genre scenario is created by grouping all the datasts as follows.
	For ex., to use same_topic and split the data 60-40 use:
	train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
	split='train[:60%]+validation[:60%]+test[:60%]')
	tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
	split='train[-40%:]+validation[-40%:]+test[-40%:]')

	IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced

	* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples
	"""

	_URL = "https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1"


	# Using a specific configuration class is optional, you can also use the base class if you don't need
	# to add specific attributes.
	# here we give an example for three sub-set of the dataset with difference sizes.
	class GuardianAuthorshipConfig(datasets.BuilderConfig):
	"""BuilderConfig for NewDataset"""

	def __init__(self, train_folder, valid_folder, test_folder, **kwargs):
	"""
	Args:
	Train_folder: Topic/genre used for training
	valid_folder: ~ ~ for validation
	test_folder: ~ ~ for testing

	**kwargs: keyword arguments forwarded to super.
	"""
	super(GuardianAuthorshipConfig, self).__init__(**kwargs)
	self.train_folder = train_folder
	self.valid_folder = valid_folder
	self.test_folder = test_folder


	class GuardianAuthorship(datasets.GeneratorBasedBuilder):
	"""dataset for same- and cross-topic authorship attribution"""

	config_counter = 0
	BUILDER_CONFIG_CLASS = GuardianAuthorshipConfig
	BUILDER_CONFIGS = [
	# cross-topic
	GuardianAuthorshipConfig(
	name=f"cross_topic_{1}",
	version=datasets.Version(f"{1}.0.0", description=f"The Original DS with the cross-topic scenario no.{1}"),
	train_folder="Politics",
	valid_folder="Society",
	test_folder="UK,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{2}",
	version=datasets.Version(f"{2}.0.0", description=f"The Original DS with the cross-topic scenario no.{2}"),
	train_folder="Politics",
	valid_folder="UK",
	test_folder="Society,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{3}",
	version=datasets.Version(f"{3}.0.0", description=f"The Original DS with the cross-topic scenario no.{3}"),
	train_folder="Politics",
	valid_folder="World",
	test_folder="Society,UK",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{4}",
	version=datasets.Version(f"{4}.0.0", description=f"The Original DS with the cross-topic scenario no.{4}"),
	train_folder="Society",
	valid_folder="Politics",
	test_folder="UK,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{5}",
	version=datasets.Version(f"{5}.0.0", description=f"The Original DS with the cross-topic scenario no.{5}"),
	train_folder="Society",
	valid_folder="UK",
	test_folder="Politics,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{6}",
	version=datasets.Version(f"{6}.0.0", description=f"The Original DS with the cross-topic scenario no.{6}"),
	train_folder="Society",
	valid_folder="World",
	test_folder="Politics,UK",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{7}",
	version=datasets.Version(f"{7}.0.0", description=f"The Original DS with the cross-topic scenario no.{7}"),
	train_folder="UK",
	valid_folder="Politics",
	test_folder="Society,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{8}",
	version=datasets.Version(f"{8}.0.0", description=f"The Original DS with the cross-topic scenario no.{8}"),
	train_folder="UK",
	valid_folder="Society",
	test_folder="Politics,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{9}",
	version=datasets.Version(f"{9}.0.0", description=f"The Original DS with the cross-topic scenario no.{9}"),
	train_folder="UK",
	valid_folder="World",
	test_folder="Politics,Society",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{10}",
	version=datasets.Version(
	f"{10}.0.0", description=f"The Original DS with the cross-topic scenario no.{10}"
	),
	train_folder="World",
	valid_folder="Politics",
	test_folder="Society,UK",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{11}",
	version=datasets.Version(
	f"{11}.0.0", description=f"The Original DS with the cross-topic scenario no.{11}"
	),
	train_folder="World",
	valid_folder="Society",
	test_folder="Politics,UK",
	),
	GuardianAuthorshipConfig(
	name=f"cross_topic_{12}",
	version=datasets.Version(
	f"{12}.0.0", description=f"The Original DS with the cross-topic scenario no.{12}"
	),
	train_folder="World",
	valid_folder="UK",
	test_folder="Politics,Society",
	),
	# # cross-genre
	GuardianAuthorshipConfig(
	name=f"cross_genre_{1}",
	version=datasets.Version(f"{1}.0.0", description=f"The Original DS with the cross-genre scenario no.{1}"),
	train_folder="Books",
	valid_folder="Politics",
	test_folder="Society,UK,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_genre_{2}",
	version=datasets.Version(f"{2}.0.0", description=f"The Original DS with the cross-genre scenario no.{2}"),
	train_folder="Books",
	valid_folder="Society",
	test_folder="Politics,UK,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_genre_{3}",
	version=datasets.Version(f"{3}.0.0", description=f"The Original DS with the cross-genre scenario no.{3}"),
	train_folder="Books",
	valid_folder="UK",
	test_folder="Politics,Society,World",
	),
	GuardianAuthorshipConfig(
	name=f"cross_genre_{4}",
	version=datasets.Version(f"{4}.0.0", description=f"The Original DS with the cross-genre scenario no.{4}"),
	train_folder="Books",
	valid_folder="World",
	test_folder="Politics,Society,UK",
	),
	]

	def _info(self):
	# Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	# These are the features of your dataset like images, labels ...
	# There are 13 authors in this dataset
	"author": datasets.features.ClassLabel(
	names=[
	"catherinebennett",
	"georgemonbiot",
	"hugoyoung",
	"jonathanfreedland",
	"martinkettle",
	"maryriddell",
	"nickcohen",
	"peterpreston",
	"pollytoynbee",
	"royhattersley",
	"simonhoggart",
	"willhutton",
	"zoewilliams",
	]
	),
	# There are book reviews, and articles on the following four topics
	"topic": datasets.features.ClassLabel(names=["Politics", "Society", "UK", "World", "Books"]),
	"article": datasets.Value("string"),
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=[("article", "author")],
	# Homepage of the dataset for documentation
	homepage="http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)

	# This folder contains the orginal/2013 dataset
	data_dir = os.path.join(dl_dir, "Guardian", "Guardian_original")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.train_folder, "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.test_folder, "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.valid_folder, "split": "valid"},
	),
	]

	def _generate_examples(self, data_dir, samples_folders, split):
	"""Yields examples."""
	# Yields (key, example) tuples from the dataset

	# Training and validation are on 1 topic/genre, while testing is on multiple topics
	# We convert the sample folders into list (from string)
	if samples_folders.count(",") == 0:
	samples_folders = [samples_folders]
	else:
	samples_folders = samples_folders.split(",")

	# the dataset is structured as:
	# \|-Topic1
	# \|---author 1
	# \|------- article-1
	# \|------- article-2
	# \|---author 2
	# \|------- article-1
	# \|------- article-2
	# \|-Topic2
	# ...

	for topic in samples_folders:
	full_path = os.path.join(data_dir, topic)

	for author in os.listdir(full_path):

	list_articles = os.listdir(os.path.join(full_path, author))
	if len(list_articles) == 0:
	# Some authors have no articles on certain topics
	continue

	for id_, article in enumerate(list_articles):
	path_2_author = os.path.join(full_path, author)
	path_2_article = os.path.join(path_2_author, article)

	with open(path_2_article, "r", encoding="utf8", errors="ignore") as f:
	art = f.readlines()

	# The whole article is stored as one line. We access the 1st element of the list
	# to store it as string, not as a list
	yield f"{topic}_{author}_{id_}", {
	"article": art[0],
	"author": author,
	"topic": topic,
	}