Datasets:
re_dial

	# 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.
	"""Annotated dataset of dialogues where users recommend movies to each other."""


	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{li2018conversational,
	title={Towards Deep Conversational Recommendations},
	author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
	booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
	year={2018}
	}
	"""

	_DESCRIPTION = """\
	ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
	recommend movies to each other. The dataset was collected by a team of researchers working at
	Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.

	The dataset allows research at the intersection of goal-directed dialogue systems
	(such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
	"""

	_HOMEPAGE = "https://redialdata.github.io/website/"

	_LICENSE = "CC BY 4.0 License."

	_DATA_URL = "https://github.com/ReDialData/website/raw/data/redial_dataset.zip"


	class ReDial(datasets.GeneratorBasedBuilder):
	"""Annotated dataset of dialogues where users recommend movies to each other."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	question_features = {
	"movieId": datasets.Value("string"),
	"suggested": datasets.Value("int32"),
	"seen": datasets.Value("int32"),
	"liked": datasets.Value("int32"),
	}
	features = datasets.Features(
	{
	"movieMentions": [
	{
	"movieId": datasets.Value("string"),
	"movieName": datasets.Value("string"),
	},
	],
	"respondentQuestions": [question_features],
	"messages": [
	{
	"timeOffset": datasets.Value("int32"),
	"text": datasets.Value("string"),
	"senderWorkerId": datasets.Value("int32"),
	"messageId": datasets.Value("int32"),
	},
	],
	"conversationId": datasets.Value("int32"),
	"respondentWorkerId": datasets.Value("int32"),
	"initiatorWorkerId": datasets.Value("int32"),
	"initiatorQuestions": [question_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."""
	data_dir = dl_manager.download_and_extract(_DATA_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train_data.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, "test_data.jsonl"), "split": "test"},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	examples = f.readlines()
	for id_, row in enumerate(examples):
	data = json.loads(row.strip())
	d = {}
	movieMentions_list = []
	for i in data["movieMentions"]:
	d["movieId"] = i
	d["movieName"] = data["movieMentions"][i]
	movieMentions_list.append(d)
	d = {}

	respondentQuestions_list = []
	for i in data["respondentQuestions"]:
	d["movieId"] = i
	alpha = data["respondentQuestions"][i]
	z = {d, alpha} # merging 2 dictionaries
	respondentQuestions_list.append(z)
	d = {}

	initiatorQuestions_list = []
	for i in data["initiatorQuestions"]:
	d["movieId"] = i
	alpha = data["initiatorQuestions"][i]
	z = {d, alpha} # merging 2 dictionaries
	initiatorQuestions_list.append(z)
	d = {}

	yield id_, {
	"movieMentions": movieMentions_list,
	"respondentQuestions": respondentQuestions_list,
	"messages": data["messages"],
	"conversationId": data["conversationId"],
	"respondentWorkerId": data["respondentWorkerId"],
	"initiatorWorkerId": data["initiatorWorkerId"],
	"initiatorQuestions": initiatorQuestions_list,
	}