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"""yelp_dataset.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/14UtK4YCjMSx4cVbUb9NBRHviWZg07dtY |
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""" |
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"""TODO: Add a description here.""" |
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import csv |
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import json |
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import os |
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from typing import List |
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import datasets |
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import logging |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {A great new dataset}, |
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author={huggingface, Inc. |
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}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
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""" |
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_HOMEPAGE = "https://www.yelp.com/dataset/download" |
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_LICENSE = "" |
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_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/" |
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_URLS = { |
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"train": _URL + "yelp_train.csv", |
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"test": _URL + "yelp_test.csv", |
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} |
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class YelpDataset(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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_URLS = _URLS |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"business_id": datasets.Value("string"), |
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"name": datasets.Value("string"), |
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"address": datasets.Value("string"), |
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"city": datasets.Value("string"), |
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"state": datasets.Value("string"), |
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"postal_code": datasets.Value("string"), |
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"latitude": datasets.Value("float"), |
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"longitude": datasets.Value("float"), |
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"stars_x": datasets.Value("float"), |
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"review_count": datasets.Value("float"), |
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"is_open": datasets.Value("int32"), |
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"categories": datasets.Value("string"), |
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"hours": datasets.Value("string"), |
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"review_id": datasets.Value("string"), |
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"user_id": datasets.Value("string"), |
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"stars_y": datasets.Value("float"), |
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"useful": datasets.Value("int32"), |
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"funny": datasets.Value("int32"), |
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"cool": datasets.Value("int32"), |
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"text": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"attributes": datasets.Value("string"), |
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}), |
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supervised_keys=None, |
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homepage="https://www.yelp.com/dataset/download", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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urls_to_download = self._URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logging.info("generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as csv_file: |
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reader = csv.DictReader(csv_file) |
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for i, row in enumerate(reader): |
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if 'is_open' in row and row['is_open']: |
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row['is_open'] = int(float(row['is_open'])) |
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yield i, row |
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