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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("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"),
}),
# 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):
# Handle missing values for float fields
for key, value in row.items():
if value == '':
# Assuming all fields that can be empty are floats; adjust as needed
row[key] = None
yield i, row
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