Datasets:
Update Yelpdata_663.py
Browse files- Yelpdata_663.py +68 -68
Yelpdata_663.py
CHANGED
@@ -59,79 +59,79 @@ _LICENSE = "https://s3-media0.fl.yelpcdn.com/assets/srv0/engineering_pages/f64cb
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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"business": _URL + "yelp_academic_dataset_business.json",
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"review": _URL + "yelp_academic_dataset_review.json",
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}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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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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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("float"),
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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("float"),
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"funny": datasets.Value("float"),
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"cool": datasets.Value("float"),
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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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# No default supervised_keys (as we have to pass both question
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# and context as input).
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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 _generate_examples(self, filepaths):
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logging.info("Generating examples from = %s", filepaths)
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# Load JSON files into pandas DataFrames
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business_df = pd.read_json(filepaths['business'], lines=True)
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review_df = pd.read_json(filepaths['review'], lines=True)
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# Merge DataFrames on 'business_id'
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merged_df = pd.merge(business_df, review_df, on='business_id')
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filtered_df = merged_df[merged_df['categories'].str.contains("Restaurants", na=False)]
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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import json
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import datasets
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class YelpDataset(datasets.GeneratorBasedBuilder):
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"""Yelp Dataset focusing on restaurant reviews."""
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VERSION = datasets.Version("1.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="restaurants", version=VERSION, description="This part of my dataset covers a wide range of restaurants"),
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]
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DEFAULT_CONFIG_NAME = "restaurants"
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_URL = "https://yelpdata.s3.us-west-2.amazonaws.com/"
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_URLS = {
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"business": _URL + "yelp_academic_dataset_business.json",
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"review": _URL + "yelp_academic_dataset_review.json",
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}
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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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"categories": 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": datasets.Value("float"),
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"text": datasets.Value("string"),
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"date": datasets.Value("string"),
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}
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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):
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"""Returns SplitGenerators."""
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downloaded_files = dl_manager.download_and_extract(self._URLS)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "train"}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "test"}),
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]
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def _generate_examples(self, business_path, review_path, split):
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"""Yields examples as (key, example) tuples."""
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# Load businesses and filter for restaurants
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with open(business_path, encoding="utf-8") as f:
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businesses = {line['business_id']: line for line in (json.loads(line) for line in f) if "Restaurants" in line.get("categories", "")}
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# Generate examples
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with open(review_path, encoding="utf-8") as f:
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for line in f:
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review = json.loads(line)
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business_id = review['business_id']
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if business_id in businesses:
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yield review['review_id'], {
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"business_id": business_id,
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"name": businesses[business_id]['name'],
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"categories": businesses[business_id]['categories'],
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"review_id": review['review_id'],
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"user_id": review['user_id'],
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"stars": review['stars'],
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"text": review['text'],
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"date": review['date'],
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}
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