# 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 re import datasets from datasets import BuilderConfig _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/" _CITATION = """@misc{ro_huang_regulatory_2023-1, author = {{Ro Huang}}, date = {2023-03-19}, publisher = {Hugging Face}, title = {Regulatory Comments {API} Call}, url = {https://huggingface.co/datasets/ro-h/regulatory_comments_api}, version = {1.1.4}, bdsk-url-1 = {https://huggingface.co/datasets/ro-h/regulatory_comments_api}} """ class RegulationsDataFetcher: BASE_COMMENT_URL = 'https://api.regulations.gov/v4/comments' BASE_DOCKET_URL = 'https://api.regulations.gov/v4/dockets/' def __init__(self, docket_id, api_key): self.docket_id = docket_id self.api_key = api_key self.docket_url = self.BASE_DOCKET_URL + docket_id self.headers = { 'X-Api-Key': self.api_key, 'Content-Type': 'application/json' } 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() elif response.status_code == 429: print(f'API Rate Limit Reached.') return None 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']) elif response.status_code == 429: print(f'API Rate Limit Reached.') return None 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() if not data: return None docket_info = self.get_docket_info() if not docket_info: return None # Starting out with docket information nested_data = { "id": self.docket_id, "agency": self.docket_id.split('-')[0], "title": docket_info[1] if docket_info else "Unknown Title", "update_date": docket_info[2].split('T')[0] if docket_info and docket_info[2] else "Unknown Update Date", "update_time": docket_info[2].split('T')[1].strip('Z') if docket_info and docket_info[2] and 'T' in docket_info[2] else "Unknown Update Time", "purpose": docket_info[3], "keywords": docket_info[4], "comments": [] } # Going into each docket for comment information if 'data' in data: for comment in data['data']: if len(nested_data["comments"]) >= 10: break comment_details = self.fetch_comment_details(comment['links']['self']) if 'data' in comment_details and 'attributes' in comment_details['data']: comment_data = comment_details['data']['attributes'] # Basic comment text cleaning comment_text = (comment_data.get('comment', '') or '').strip() comment_text = comment_text.replace("
", "").replace("", "") comment_text = re.sub(r'&[^;]+;', '', comment_text) # Recording detailed comment information if (comment_text and "attached" not in comment_text.lower() and "attachment" not in comment_text.lower() and comment_text.lower() != "n/a"): nested_comment = { "text": comment_text, "comment_id": comment['id'], "comment_url": comment['links']['self'], "comment_date": comment['attributes']['postedDate'].split('T')[0], "comment_time": comment['attributes']['postedDate'].split('T')[1].strip('Z'), "commenter_fname": ((comment_data.get('firstName') or 'Anonymous').split(',')[0]).capitalize(), "commenter_lname": ((comment_data.get('lastName') or 'Anonymous').split(',')[0]).capitalize(), "comment_length": len(comment_text) if comment_text is not None else 0 } nested_data["comments"].append(nested_comment) return nested_data class RegCommentsAPIConfig(BuilderConfig): def __init__(self, api_key=None, docket_ids = None, **kwargs): self.api_key = api_key self.docket_ids = docket_ids super(RegCommentsAPIConfig, self).__init__(**kwargs) class RegCommentsAPI(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ RegCommentsAPIConfig( name="default", version=datasets.Version("1.0.0"), description="Dataset of regulatory comments" ) ] BUILDER_CONFIG_CLASS = RegCommentsAPIConfig # Method to define the structure of the dataset def _info(self): # Defining the structure of the dataset features = datasets.Features({ "id": datasets.Value("string"), "agency": 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"), "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, citation = _CITATION ) def _split_generators(self, dl_manager): # Retrieve the API key from the builder's config api_key = self.config.api_key docket_ids = self.config.docket_ids # Define your dataset's splits. In this case, only a training split. return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "api_key": api_key, # Pass the API key to the generator function "docket_ids": docket_ids }, ), ] def _generate_examples(self, api_key, docket_ids): # Iterate over each search term to fetch relevant dockets dockets = docket_ids for docket_id in dockets: fetcher = RegulationsDataFetcher(docket_id, api_key) # Initialize with the API key docket_data = fetcher.collect_data() if docket_data is None: print(f"Stopping Data Collection.") break if len(docket_data["comments"]) != 0: yield docket_id, docket_data