Datasets:
glucose

	# 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.
	"""GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes."""


	import csv
	import os

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{mostafazadeh2020glucose,
	title={GLUCOSE: GeneraLized and COntextualized Story Explanations},
	author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll},
	year={2020},
	booktitle={The Conference on Empirical Methods in Natural Language Processing},
	publisher={Association for Computational Linguistics}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.
	"""

	_HOMEPAGE = "https://github.com/ElementalCognition/glucose"

	_LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License"

	_URLs = {
	"glucose": {
	"test": "https://raw.githubusercontent.com/ElementalCognition/glucose/master/test/test_set_no_answers.csv",
	"train": "https://github.com/TevenLeScao/glucose/blob/master/GLUCOSE_training_data.zip?raw=true",
	}
	}


	class Glucose(datasets.GeneratorBasedBuilder):
	"""GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning."""

	VERSION = datasets.Version("1.1.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="glucose", description="Main dataset"),
	]

	def _info(self):
	feature_dict = {
	"experiment_id": datasets.Value("string"),
	"story_id": datasets.Value("string"),
	# The train set contains only one ID in numeric form
	"worker_id": datasets.Value("int64"),
	# The test set contains several IDs in string form
	"worker_ids": datasets.Value("string"),
	"submission_time_normalized": datasets.Value("string"),
	"worker_quality_assessment": datasets.Value("int64"),
	"selected_sentence_index": datasets.Value("int64"),
	"story": datasets.Value("string"),
	"selected_sentence": datasets.Value("string"),
	"number_filled_in": datasets.Value("int64"),
	}
	for i in range(1, 11):
	feature_dict[f"{i}_specificNL"] = datasets.Value("string")
	feature_dict[f"{i}_specificStructured"] = datasets.Value("string")
	feature_dict[f"{i}_generalNL"] = datasets.Value("string")
	feature_dict[f"{i}_generalStructured"] = datasets.Value("string")
	features = datasets.Features(feature_dict)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	train_url = _URLs[self.config.name]["train"]
	test_url = _URLs[self.config.name]["test"]
	train_data = dl_manager.download_and_extract(train_url)
	test_data = dl_manager.download_and_extract(test_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(train_data, "GLUCOSE_training_data_final.csv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": test_data, "split": "test"},
	),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf8") as f:
	data = csv.reader(f)
	next(data)
	for id_, row in enumerate(data):
	if split == "train":
	yield id_, train_dict_from_row(row)
	else:
	yield id_, test_dict_from_row(row)


	def train_dict_from_row(row):
	return_dict = {
	"experiment_id": row[0],
	"story_id": row[1],
	"worker_id": row[2],
	"worker_ids": "",
	"submission_time_normalized": row[3],
	"worker_quality_assessment": row[4],
	"selected_sentence_index": row[5],
	"story": row[6],
	"selected_sentence": row[7],
	"number_filled_in": row[48],
	}
	for i in range(1, 11):
	return_dict[f"{i}_specificNL"] = row[4 * i + 4]
	return_dict[f"{i}_specificStructured"] = row[4 * i + 5]
	return_dict[f"{i}_generalNL"] = row[4 * i + 6]
	return_dict[f"{i}_generalStructured"] = row[4 * i + 7]
	return return_dict


	def test_dict_from_row(row):
	return_dict = {
	"experiment_id": "",
	"story_id": row[0],
	"worker_id": -1,
	"worker_ids": row[3],
	"submission_time_normalized": "",
	"worker_quality_assessment": -1,
	"selected_sentence_index": -1,
	"story": row[1],
	"selected_sentence": row[2],
	"number_filled_in": -1,
	}
	for i in range(1, 11):
	return_dict[f"{i}_specificNL"] = row[2 * i + 2]
	return_dict[f"{i}_generalNL"] = row[2 * i + 3]
	return_dict[f"{i}_specificStructured"] = ""
	return_dict[f"{i}_generalStructured"] = ""
	return return_dict