Yelpdata_663.py

# -*- 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."""

!pip install datasets

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):
      raise ValueError('woops!')
      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"),
                  "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"),
                      }
                  ),
                  "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"),

              }
          ),
          # 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