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."""


import csv
import json
import os
from typing import List
import datasets
import logging
import pandas as pd

# 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 dataset encompasses a wealth of information from the Yelp platform,
 detailing user reviews, business ratings, and operational specifics across a diverse array of local establishments.
"""

# 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 = "https://s3-media0.fl.yelpcdn.com/assets/srv0/engineering_pages/f64cb2d3efcc/assets/vendor/Dataset_User_Agreement.pdf"

# 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 = {
    "business": _URL + "yelp_academic_dataset_business.json",
    "review": _URL + "yelp_academic_dataset_review.json",
}
# 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 _generate_examples(self, filepaths):
        logging.info("Generating examples from = %s", filepaths)
    
    # Load JSON files into pandas DataFrames
        business_df = pd.read_json(filepaths['business'], lines=True)
        review_df = pd.read_json(filepaths['review'], lines=True)
    
    # Merge DataFrames on 'business_id'
        merged_df = pd.merge(business_df, review_df, on='business_id')
    
    # Filter out entries where 'categories' does not contain 'Restaurants'
        filtered_df = merged_df[merged_df['categories'].str.contains("Restaurants", na=False)]
    
    # Convert to CSV (optional step if you need CSV output)
    # filtered_df.to_csv('filtered_dataset.csv', index=False)
    
    # Generate examples
        for index, row in filtered_df.iterrows():
        # Handle missing values for float fields
            for key, value in row.items():
                if pd.isnull(value):
                    row[key] = None  # or appropriate handling of nulls based on your requirements
        
        # Yield each row as an example
            yield index, row.to_dict()