Upload regulatory_comments_api.py

regulatory_comments_api.py

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+# 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.
+
+
+import requests
+import datasets
+
+_DESCRIPTION = """\
+United States governmental agencies often make proposed regulations open to the public for comment. 
+Proposed regulations are organized into "dockets". This dataset will use Regulation.gov public API 
+to aggregate and clean public comments for dockets that mention opioid use. 
+
+Each example will consist of one docket, and include metadata such as docket id, docket title, etc. 
+Each docket entry will also include information about the top 10 comments, including comment metadata
+and comment text. 
+"""
+
+# Homepage URL of the dataset
+_HOMEPAGE = "https://www.regulations.gov/"
+
+# URL to download the dataset
+_URLS = {"url": "https://huggingface.co/datasets/ro-h/regulatory_comments/raw/main/docket_comments_all.json"}
+
+class RegulationsDataFetcher:
+    API_KEY = "IsH1c1CAB0CR8spovnnx2INbLz8gQlVkbmXYII2z" #'4T29l93SvmnyNCVFZUFzSfUqTq6k7S0Wqn93sLcH'
+    BASE_COMMENT_URL = 'https://api.regulations.gov/v4/comments'
+    BASE_DOCKET_URL = 'https://api.regulations.gov/v4/dockets/'
+    HEADERS = {
+        'X-Api-Key': API_KEY,
+        'Content-Type': 'application/json'
+    }
+
+    def __init__(self, docket_id):
+        self.docket_id = docket_id
+        self.docket_url = self.BASE_DOCKET_URL + docket_id
+        self.dataset = []
+
+    def fetch_comments(self):
+        """Fetch a single page of 25 comments."""
+        url = f'{self.BASE_COMMENT_URL}?filter[docketId]={self.docket_id}&page[number]=1&page[size]=25'
+        response = requests.get(url, headers=self.HEADERS)
+        
+        if response.status_code == 200:
+            return response.json()
+        else:
+            print(f'Failed to retrieve comments: {response.status_code}')
+            return None
+
+    def get_docket_info(self):
+        """Get docket information."""
+        response = requests.get(self.docket_url, headers=self.HEADERS)
+        
+        if response.status_code == 200:
+            docket_data = response.json()
+            return (docket_data['data']['attributes']['agencyId'],
+                    docket_data['data']['attributes']['title'],
+                    docket_data['data']['attributes']['modifyDate'], 
+                    docket_data['data']['attributes']['docketType'], 
+                    docket_data['data']['attributes']['keywords'])
+        else:
+            print(f'Failed to retrieve docket info: {response.status_code}')
+            return None
+
+    def fetch_comment_details(self, comment_url):
+        """Fetch detailed information of a comment."""
+        response = requests.get(comment_url, headers=self.HEADERS)
+        if response.status_code == 200:
+            return response.json()
+        else:
+            print(f'Failed to retrieve comment details: {response.status_code}')
+            return None
+
+    def collect_data(self):
+        """Collect data and reshape into nested dictionary format."""
+        data = self.fetch_comments()
+        docket_info = self.get_docket_info()
+
+        # Initialize the nested dictionary structure
+        nested_data = {
+            "id": self.docket_id,
+            "title": docket_info[1] if docket_info else "Unknown Title",
+            "context": docket_info[2] if docket_info else "Unknown Context",
+            "purpose": docket_info[3],
+            "keywords": docket_info[4],
+            "comments": []
+        }
+
+        if data and 'data' in data:
+            for comment in data['data']:
+                comment_details = self.fetch_comment_details(comment['links']['self'])
+                
+                if comment_details and 'data' in comment_details and 'attributes' in comment_details['data']:
+                    comment_data = comment_details['data']['attributes']
+                    nested_comment = {
+                        "text": comment_data.get('comment', ''),
+                        "comment_id": comment['id'],
+                        "comment_url": comment['links']['self'],
+                        "comment_date": comment['attributes']['postedDate'],
+                        "comment_title": comment['attributes']['title'],
+                        "commenter_fname": comment_data.get('firstName', ''),
+                        "commenter_lname": comment_data.get('lastName', ''),
+                        "comment_length": len(comment_data.get('comment', '')) if comment_data.get('comment') is not None else 0
+                    }
+                    nested_data["comments"].append(nested_comment)
+
+                if len(nested_data["comments"]) >= 10:
+                    break
+
+        return nested_data
+
+class RegComments(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+
+    # Method to define the structure of the dataset
+    def _info(self):
+        # Defining the structure of the dataset
+        features = datasets.Features({
+            "id": datasets.Value("string"),
+            "title": datasets.Value("string"),
+            "context": datasets.Value("string"),
+            "purpose": datasets.Value("string"),
+            "keywords": datasets.Sequence(datasets.Value("string")),
+            "comments": datasets.Sequence({
+                "text": datasets.Value("string"),
+                "comment_id": datasets.Value("string"),
+                "comment_url": datasets.Value("string"),
+                "comment_date": datasets.Value("string"),
+                "comment_title": datasets.Value("string"),
+                "commenter_fname": datasets.Value("string"),
+                "commenter_lname": datasets.Value("string"),
+                "comment_length": datasets.Value("int32")
+            })
+        })
+
+        # Returning the dataset structure
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE
+        )
+    
+    def _split_generators(self, dl_manager):
+        # Expect an API key to be passed as a parameter
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "search_terms": opioid_related_terms,
+                    "api_key": self.config.api_key,  # Use the API key provided by the user
+                },
+            ),
+        ]
+
+    def _generate_examples(self, search_terms, api_key):
+        # Iterate over each search term to fetch relevant dockets
+        for term in search_terms:
+            docket_ids = get_docket_ids(term, api_key)  # Pass the API key here
+            for docket_id in docket_ids:
+                fetcher = RegulationsDataFetcher(docket_id, api_key)  # Initialize with the API key
+                docket_data = fetcher.collect_data()
+                if len(docket_data["comments"]) != 0:
+                    yield docket_id, docket_data
+
+# Modify the get_docket_ids function to accept an API key
+def get_docket_ids(search_term, api_key):
+    url = f"https://api.regulations.gov/v4/dockets"
+    params = {
+        'filter[searchTerm]': search_term,
+        'api_key': api_key
+    }
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        data = response.json()
+        dockets = data['data']
+        docket_ids = [docket['id'] for docket in dockets]
+        return docket_ids
+    else:
+        return f"Error: {response.status_code}"
+    
+opioid_related_terms = [
+    # Types of Opioids
+    "opioids",
+    "heroin", 
+    "morphine", 
+    "fentanyl", 
+    "methadone", 
+    "oxycodone", 
+    "hydrocodone", 
+    "codeine", 
+    "tramadol", 
+    "prescription opioids", 
+    # Withdrawal Support
+    "lofexidine", 
+    "buprenorphine", 
+    "naloxone", 
+    # Related Phrases
+    "opioid epidemic", 
+    "opioid abuse", 
+    "opioid crisis", 
+    "opioid overdose"
+    "opioid tolerance", 
+    "opioid treatment program", 
+    "medication assisted treatment", 
+]