Datasets:
coarse_discourse

	"""TODO(coarse_discourse): Add a description here."""


	import json
	import os

	import datasets


	# TODO(coarse_discourse): BibTeX citation
	_CITATION = """\
	@inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} }
	"""

	# TODO(coarse_discourse):
	_DESCRIPTION = """\
	dataset contains discourse annotation and relation on threads from reddit during 2016
	"""
	_URL = "https://github.com/google-research-datasets/coarse-discourse/archive/master.zip"


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

	# TODO(coarse_discourse): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(coarse_discourse): Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	# These are the features of your dataset like images, labels ...
	"title": datasets.Value("string"),
	"is_self_post": datasets.Value("bool"),
	"subreddit": datasets.Value("string"),
	"url": datasets.Value("string"),
	"majority_link": datasets.Value("string"),
	"is_first_post": datasets.Value("bool"),
	"majority_type": datasets.Value("string"),
	"id_post": datasets.Value("string"),
	"post_depth": datasets.Value("int32"),
	"in_reply_to": datasets.Value("string"),
	"annotations": datasets.features.Sequence(
	{
	"annotator": datasets.Value("string"),
	"link_to_post": datasets.Value("string"),
	"main_type": 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=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/google-research-datasets/coarse-discourse",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(coarse_discourse): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "coarse-discourse-master", "coarse_discourse_dataset.json")
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(coarse_discourse): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	url = data.get("url", "")
	is_self_post = data.get("is_self_post", "")
	subreddit = data.get("subreddit", "")
	title = data.get("title", "")
	posts = data.get("posts", "")
	for id1, post in enumerate(posts):
	maj_link = post.get("majority_link", "")
	maj_type = post.get("majority_type", "")
	id_post = post.get("id", "")
	is_first_post = post.get("is_firs_post", "")
	post_depth = post.get("post_depth", -1)
	in_reply_to = post.get("in_reply_to", "")
	annotations = post["annotations"]
	annotators = [annotation.get("annotator", "") for annotation in annotations]
	main_types = [annotation.get("main_type", "") for annotation in annotations]
	link_posts = [annotation.get("linkk_to_post", "") for annotation in annotations]

	yield str(id_) + "_" + str(id1), {
	"title": title,
	"is_self_post": is_self_post,
	"subreddit": subreddit,
	"url": url,
	"majority_link": maj_link,
	"is_first_post": is_first_post,
	"majority_type": maj_type,
	"id_post": id_post,
	"post_depth": post_depth,
	"in_reply_to": in_reply_to,
	"annotations": {"annotator": annotators, "link_to_post": link_posts, "main_type": main_types},
	}