Datasets:

Johnnyeee
/

Yelpdata_663

	# 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.
	# TODO: Address all TODOs and remove all explanatory comments
	"""TODO: Add a description here."""


	import csv
	import json
	import os
	from typing import List
	import datasets
	import logging
	from random import random

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2020}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://www.yelp.com/dataset/download"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""


	class YelpDataset(datasets.GeneratorBasedBuilder):
	"""Yelp Dataset focusing on restaurant reviews and business information."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="restaurants", version=VERSION, description="This part of the dataset covers a wide range of restaurants"),
	]

	DEFAULT_CONFIG_NAME = "restaurants"

	_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/"
	_URLS = {
	"business": _URL + "yelp_academic_dataset_business.json",
	"review": _URL + "yelp_academic_dataset_review.json",
	}

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({
	"business_id": datasets.Value("string"),
	"name": datasets.Value("string"),
	"address": datasets.Value("string"),
	"city": datasets.Value("string"),
	"state": datasets.Value("string"),
	"postal_code": datasets.Value("string"),
	"latitude": datasets.Value("float"),
	"longitude": datasets.Value("float"),
	"stars_x": datasets.Value("float"),
	"review_count": datasets.Value("float"),
	"is_open": datasets.Value("float"),
	"categories": datasets.Value("string"),
	"hours": datasets.Value("string"),
	"review_id": datasets.Value("string"),
	"user_id": datasets.Value("string"),
	"stars_y": datasets.Value("float"),
	"useful": datasets.Value("float"),
	"funny": datasets.Value("float"),
	"cool": datasets.Value("float"),
	"text": datasets.Value("string"),
	"date": datasets.Value("string"),
	"attributes": datasets.Value("string"),
	}),
	supervised_keys=None,
	homepage="https://www.yelp.com/dataset/download",
	citation=_CITATION,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	"""Returns SplitGenerators."""
	downloaded_files = dl_manager.download_and_extract(self._URLS)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": downloaded_files, "split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": downloaded_files, "split": "test"}),
	]


	def _generate_examples(self, files, split):
	"""Yields examples as (key, example) tuples."""
	business_path, review_path = files["business"], files["review"]

	# Load businesses and filter for restaurants
	with open(business_path, encoding="utf-8") as f:
	businesses = {}
	for line in f:
	business = json.loads(line)
	# Check if 'categories' is not None and contains "Restaurants"
	if business.get("categories") and "Restaurants" in business["categories"]:
	businesses[business['business_id']] = business

	# Generate examples with an attempted 80/20 split for train/test
	with open(review_path, encoding="utf-8") as f:
	for line in f:
	review = json.loads(line)
	business_id = review['business_id']
	if business_id in businesses:
	business = businesses[business_id]
	example = {
	"business_id": business['business_id'],
	"name": business.get("name", ""),
	"address": business.get("address", ""),
	"city": business.get("city", ""),
	"state": business.get("state", ""),
	"postal_code": business.get("postal_code", ""),
	"latitude": business.get("latitude", None),
	"longitude": business.get("longitude", None),
	"stars_x": business.get("stars", None),
	"review_count": business.get("review_count", None),
	"is_open": business.get("is_open", None),
	"categories": business.get("categories", ""),
	"hours": json.dumps(business.get("hours", {})), # Storing hours as a JSON string
	"review_id": review.get("review_id", ""),
	"user_id": review.get("user_id", ""),
	"stars_y": review.get("stars", None),
	"useful": review.get("useful", None),
	"funny": review.get("funny", None),
	"cool": review.get("cool", None),
	"text": review.get("text", ""),
	"date": review.get("date", ""),
	"attributes": json.dumps(business.get("attributes", {})), # Storing attributes as a JSON string
	}
	# Randomly assign to split based on an 80/20 ratio
	if (split == 'train' and random() < 0.8) or (split == 'test' and random() >= 0.8):
	yield review['review_id'], example