Datasets:

peixian
/

rtGender

	# 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.
	"""Class for loading datafrom rtGender"""

	from __future__ import absolute_import, division, print_function

	import csv
	from enum import Enum
	import os

	import datasets


	_CITATION = """\
	@inproceedings{voigt-etal-2018-rtgender,
	title = "{R}t{G}ender: A Corpus for Studying Differential Responses to Gender",
	author = "Voigt, Rob and
	Jurgens, David and
	Prabhakaran, Vinodkumar and
	Jurafsky, Dan and
	Tsvetkov, Yulia",
	booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
	month = may,
	year = "2018",
	address = "Miyazaki, Japan",
	publisher = "European Language Resources Association (ELRA)",
	url = "https://www.aclweb.org/anthology/L18-1445",
	}
	"""

	_DESCRIPTION = """\
	RtGender is a corpus for studying responses to gender online, including posts and responses from Facebook, TED, Fitocracy, and Reddit where the gender of the source poster/speaker is known.
	"""

	_HOMEPAGE = "https://nlp.stanford.edu/robvoigt/rtgender/#contact"

	_LICENSE = "Research Only"

	_URL = "https://nlp.stanford.edu/robvoigt/rtgender/rtgender.tar.gz"


	class Config(Enum):
	ANNOTATIONS = "annotations"
	POSTS = "posts"
	RESPONSES = "responses"
	FB_POLI = "fb_politicians"
	FB_PUB = "fb_public"
	TED = "ted"
	FITOCRACY = "fitocracy"
	REDDIT = "reddit"


	class rtGender(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=Config.ANNOTATIONS.value,
	version=VERSION,
	description="Retrieves only the annotations.",
	),
	datasets.BuilderConfig(
	name=Config.POSTS.value,
	version=VERSION,
	description="Retrieves all posts.",
	),
	datasets.BuilderConfig(
	name=Config.RESPONSES.value,
	version=VERSION,
	description="Retrieves all responses.",
	)
	]
	DEFAULT_CONFIG_NAME = Config.ANNOTATIONS.value # It's not mandatory to have a default configuration. Just use one if it make sense.

	POSTS_FEATURES = {
	"source": datasets.Value("string"),
	"op_id": datasets.Value("string"),
	"op_gender": datasets.Value("string"),
	"post_id": datasets.Value("string"),
	"post_text": datasets.Value("string"),
	"post_type": datasets.Value("string"), # only for fb
	"subreddit": datasets.Value("string"), # only for reddit
	"op_gender_visible": datasets.Value("string"), # only for reddit
	}

	RESPONSES_FEATURES = {
	"source": datasets.Value("string"),
	"op_id": datasets.Value("string"),
	"op_gender": datasets.Value("string"),
	"post_id": datasets.Value("string"),
	"responder_id": datasets.Value("string"),
	"response_text": datasets.Value("string"),
	"op_name": datasets.Value("string"), # only for fb
	"op_category": datasets.Value("string"), # only for fb
	"responder_gender": datasets.Value("string"), # only for fitocracy and reddit
	"responder_gender_visible": datasets.Value("string"), # only for reddit
	"subreddit": datasets.Value("string"),
	}

	ANNOTATION_FEATURES = {
	"source": datasets.Value("string"),
	"op_gender": datasets.Value("string"),
	"post_text": datasets.Value("string"),
	"response_text": datasets.Value("string"),
	"sentiment": datasets.Value("string"),
	"relevance": datasets.Value("string"),
	}

	def _info(self):

	if (
	self.config.name == Config.ANNOTATIONS.value
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(self.ANNOTATION_FEATURES)
	elif self.config.name == Config.POSTS.value:
	features = datasets.Features(self.POSTS_FEATURES)
	else:
	features = datasets.Features(self.RESPONSES_FEATURES)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# 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=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	data_dir = dl_manager.download_and_extract(_URL)
	if self.config.name == Config.ANNOTATIONS.value:
	files = ["annotations.csv"]
	elif self.config.name == Config.POSTS.value:
	files = [
	"facebook_congress_posts.csv",
	"facebook_wiki_posts.csv",
	"fitocracy_posts.csv",
	"reddit_posts.csv",
	]
	else:
	files = [
	"facebook_congress_responses.csv",
	"facebook_wiki_responses.csv",
	"fitocracy_responses.csv",
	"reddit_responses.csv",
	"ted_responses.csv",
	]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepaths": list(map(lambda x: os.path.join(data_dir, x), files)),
	"split": "train",
	},
	),
	]

	def _generate_examples(
	self,
	filepaths,
	split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.
	files = []
	readers = {}
	for fp in filepaths:
	f = open(fp, encoding="utf-8")
	reader = csv.reader(f)
	next(reader)
	readers[fp.replace(".csv", "")] = reader
	files.append(f)

	id_ = 0
	for reader_name, reader in readers.items():
	for row in reader:
	if self.config.name == Config.ANNOTATIONS.value:
	yield id_, {
	"source": row[0],
	"op_gender": row[1],
	"post_text": row[2],
	"response_text": row[3],
	"sentiment": row[4],
	"relevance": row[5],
	}
	elif self.config.name == Config.POSTS.value:
	r = {
	"source": reader_name,
	"op_id": row[0],
	"op_gender": row[1],
	"post_id": row[2],
	"post_text": row[3],
	"post_type": None,
	"subreddit": None,
	"op_gender_visible": None,
	}
	if "facebook" in reader_name:
	r["post_type"] = row[4]
	elif "reddit" in reader_name:
	r["subreddit"] = row[4]
	r["op_gender_visible"] = row[5]

	yield id_, r

	else:
	r = {
	"source": reader_name,
	"op_id": row[0],
	"op_gender": row[1],
	"post_id": row[2],
	"responder_id": row[3],
	"response_text": row[4],
	"op_name": None,
	"op_category": None,
	"responder_gender": None,
	"responder_gender_visible": None,
	"subreddit": None
	}
	if "facebook" in reader_name:
	r["op_name"] = row[5]
	r["op_category"] = row[6]
	elif "fitocracy" in reader_name:
	r["responder_gender"] = row[5]
	elif "reddit" in reader_name:
	r["subreddit"] = row[5]
	r["responder_gender"] = row[6]
	r["responder_gender_visible"] = row[7]
	yield id_, r
	id_ += 1

	for fd in files:
	fd.close()