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# -*- 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 |