# -*- coding: utf-8 -*- """yelp_dataset.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/14UtK4YCjMSx4cVbUb9NBRHviWZg07dtY """ # 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 # 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 = "" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://yelpdata.s3.us-west-2.amazonaws.com/" _URLS = { "train": _URL + "yelp_train.csv", "test": _URL + "yelp_test.csv", } # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class YelpDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" _URLS = _URLS VERSION = datasets.Version("1.1.0") 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("float64"), "longitude": datasets.Value("float64"), "stars_x": datasets.Value("float64"), "review_count": datasets.Value("int64"), "is_open": datasets.Value("int64"), "categories": datasets.Value("string"), "hours": datasets.Value("string"), "review_id": datasets.Value("string"), "user_id": datasets.Value("string"), "stars_y": datasets.Value("float64"), "useful": datasets.Value("int64"), "funny": datasets.Value("int64"), "cool": datasets.Value("int64"), "text": datasets.Value("string"), "date": datasets.Value("string"), "attributes": datasets.features.Sequence( { "RestaurantsDelivery":datasets.Value("boolean"), "OutdoorSeating":datasets.Value("boolean"), "BusinessAcceptsCreditCards":datasets.Value("boolean"), "BusinessParking": datasets.features.Sequence( {'garage':datasets.Value("boolean"), 'street':datasets.Value("boolean"), 'validated':datasets.Value("boolean"), 'lot':datasets.Value("boolean"), 'valet':datasets.Value("boolean")}), "BikeParking":datasets.Value("boolean"), "RestaurantsPriceRange2":datasets.Value("int64"), "RestaurantsTakeOut":datasets.Value("boolean"), "ByAppointmentOnly":datasets.Value("boolean"), "WiFi":datasets.Value("string"), "Alcohol":datasets.Value("string"), "Caters":datasets.Value("boolean"), 'Corkage':datasets.Value("boolean"), 'WheelchairAccessible':datasets.Value("boolean"), 'HasTV':datasets.Value("boolean"), 'Open24Hours':datasets.Value("boolean"), 'BikeParking':datasets.Value("boolean"), 'Ambience': datasets.features.Sequence( {'touristy': datasets.Value("boolean"), 'hipster': datasets.Value("boolean"), 'romantic': datasets.Value("boolean"), 'divey': datasets.Value("boolean"), 'intimate': datasets.Value("boolean"), 'trendy': datasets.Value("boolean"), 'upscale': datasets.Value("boolean"), 'classy': datasets.Value("boolean"), 'casual': datasets.Value("boolean")}), 'RestaurantsAttire': datasets.Value("string"), 'DriveThru':datasets.Value("boolean"), 'BusinessAcceptsBitcoin':datasets.Value("boolean"), 'NoiseLevel': datasets.Value("string"), 'Smoking': datasets.Value("string"), 'BestNights':datasets.features.Sequence( {u'monday': datasets.Value("boolean"), u'tuesday': datasets.Value("boolean"), u'wednesday': datasets.Value("boolean"), u'thursday': datasets.Value("boolean"), u'friday': datasets.Value("boolean"), u'saturday': datasets.Value("boolean"), u'sunday': datasets.Value("boolean")}), 'GoodForMeal':datasets.features.Sequence( {'dessert': datasets.Value("boolean"), 'latenight': datasets.Value("boolean"), 'lunch': datasets.Value("boolean"), 'dinner': datasets.Value("boolean"), 'brunch': datasets.Value("boolean"), 'breakfast': datasets.Value("boolean")}), 'RestaurantsGoodForGroups':datasets.Value("boolean"), 'GoodForDancing':datasets.Value("boolean"), 'Music':datasets.features.Sequence( {'dj': datasets.Value("boolean"), 'background_music': datasets.Value("boolean"), 'no_music': datasets.Value("boolean"), 'jukebox': datasets.Value("boolean"), 'live': datasets.Value("boolean"), 'video': datasets.Value("boolean"), 'karaoke': datasets.Value("boolean")}), 'DietaryRestrictions':datasets.features.Sequence( {'dairy-free': datasets.Value("boolean"), 'gluten-free': datasets.Value("boolean"), 'vegan': datasets.Value("boolean"), 'kosher': datasets.Value("boolean"), 'halal': datasets.Value("boolean"), 'soy-free': datasets.Value("boolean"), 'vegetarian': datasets.Value("boolean")}), 'RestaurantsReservations':datasets.Value("boolean"), 'HairSpecializesIn':datasets.features.Sequence( {'straightperms': datasets.Value("boolean"), 'coloring': datasets.Value("boolean"), 'extensions': datasets.Value("boolean"), 'africanamerican': datasets.Value("boolean"), 'curly': datasets.Value("boolean"), 'kids': datasets.Value("boolean"), 'perms': datasets.Value("boolean"), 'asian': datasets.Value("boolean")}), 'BYOBCorkage': datasets.Value("string"), 'BYOB':datasets.Value("boolean"), 'DogsAllowed':datasets.Value("boolean"), 'RestaurantsCounterService':datasets.Value("boolean"), 'RestaurantsTableService':datasets.Value("boolean"), 'CoatCheck':datasets.Value("boolean"), 'AgesAllowed': datasets.Value("string"), 'AcceptsInsurance':datasets.Value("boolean"), 'HappyHour':datasets.Value("boolean"), 'GoodForKids':datasets.Value("boolean"), } ), } ), # No default supervised_keys (as we have to pass both question # and context as input). supervised_keys=None, homepage="https://www.yelp.com/dataset/download", citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: urls_to_download = self._URLS downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logging.info("generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as csv_file: reader = csv.DictReader(csv_file) for i, row in enumerate(reader): # Convert the row to a dictionary, removing any null values example = {key: value for key, value in row.items() if value is not None} yield i, example