Update Yelpdata_663.py

Yelpdata_663.py

@@ -59,79 +59,79 @@ _LICENSE = "https://s3-media0.fl.yelpcdn.com/assets/srv0/engineering_pages/f64cb
 # 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")
+import json
+import datasets
 
-    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')
+class YelpDataset(datasets.GeneratorBasedBuilder):
+    """Yelp Dataset focusing on restaurant reviews."""
     
-    # Filter out entries where 'categories' does not contain 'Restaurants'
-        filtered_df = merged_df[merged_df['categories'].str.contains("Restaurants", na=False)]
+    VERSION = datasets.Version("1.1.0")
     
-    # Convert to CSV (optional step if you need CSV output)
-    # filtered_df.to_csv('filtered_dataset.csv', index=False)
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="restaurants", version=VERSION, description="This part of my dataset covers a wide range of restaurants"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "restaurants"
     
-    # 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
+    _URL = "https://yelpdata.s3.us-west-2.amazonaws.com/"
+    _URLS = {
+        "business": _URL + "yelp_academic_dataset_business.json",
+        "review": _URL + "yelp_academic_dataset_review.json",
+    }
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "business_id": datasets.Value("string"),
+                    "name": datasets.Value("string"),
+                    "categories": datasets.Value("string"),
+                    "review_id": datasets.Value("string"),
+                    "user_id": datasets.Value("string"),
+                    "stars": datasets.Value("float"),
+                    "text": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.yelp.com/dataset/download",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """Returns SplitGenerators."""
+        downloaded_files = dl_manager.download_and_extract(self._URLS)
         
-        # Yield each row as an example
-            yield index, row.to_dict()
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"business_path": downloaded_files["business"], "review_path": downloaded_files["review"], "split": "test"}),
+        ]
+
+    def _generate_examples(self, business_path, review_path, split):
+        """Yields examples as (key, example) tuples."""
+        
+        # Load businesses and filter for restaurants
+        with open(business_path, encoding="utf-8") as f:
+            businesses = {line['business_id']: line for line in (json.loads(line) for line in f) if "Restaurants" in line.get("categories", "")}
+        
+        # Generate examples
+        with open(review_path, encoding="utf-8") as f:
+            for line in f:
+                review = json.loads(line)
+                business_id = review['business_id']
+                if business_id in businesses:
+                    yield review['review_id'], {
+                        "business_id": business_id,
+                        "name": businesses[business_id]['name'],
+                        "categories": businesses[business_id]['categories'],
+                        "review_id": review['review_id'],
+                        "user_id": review['user_id'],
+                        "stars": review['stars'],
+                        "text": review['text'],
+                        "date": review['date'],
+                    }
+