|
|
|
"""yelp_dataset.ipynb |
|
|
|
Automatically generated by Colaboratory. |
|
|
|
Original file is located at |
|
https://colab.research.google.com/drive/14UtK4YCjMSx4cVbUb9NBRHviWZg07dtY |
|
""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""TODO: Add a description here.""" |
|
|
|
|
|
import csv |
|
import json |
|
import os |
|
from typing import List |
|
import datasets |
|
import logging |
|
|
|
|
|
|
|
_CITATION = """\ |
|
@InProceedings{huggingface:dataset, |
|
title = {A great new dataset}, |
|
author={huggingface, Inc. |
|
}, |
|
year={2020} |
|
} |
|
""" |
|
|
|
|
|
|
|
_DESCRIPTION = """\ |
|
This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
|
""" |
|
|
|
|
|
_HOMEPAGE = "https://www.yelp.com/dataset/download" |
|
|
|
|
|
_LICENSE = "" |
|
|
|
|
|
|
|
|
|
_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/" |
|
_URLS = { |
|
"train": _URL + "yelp_train.csv", |
|
"test": _URL + "yelp_test.csv", |
|
} |
|
|
|
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"), |
|
}), |
|
|
|
|
|
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): |
|
|
|
for key, value in row.items(): |
|
if value == '': |
|
|
|
row[key] = None |
|
yield i, row |
|
|
|
|