regulatory_comments_api.py

# 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/"

# URL to download the dataset
_URLS = {"url": "https://huggingface.co/datasets/ro-h/regulatory_comments/raw/main/docket_comments_all.json"}

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 data is None:
    #         return None  

    #     docket_info = self.get_docket_info()
    #     if docket_info is None:
    #         return None

    #     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": []
    #     }

    #     if 'data' in data:
    #         for comment in data['data']:
    #             comment_details = self.fetch_comment_details(comment['links']['self'])

    #             # Here, we extra information and perform cleaning. 
    #             if 'data' in comment_details and 'attributes' in comment_details['data']:
    #                 comment_data = comment_details['data']['attributes']
    #                 comment_text = (comment_data.get('comment', '') or '').strip()
    #                 comment_text = (comment_text # Doing some extra cleaning
    #                                 .replace("<br/>", "")
    #                                 .replace("<span style='padding-left: 30px'></span>", "")
    #                                 .replace(re.sub(r'&[^;]+;', '', comment_text)))

    #                 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)

    #             if len(nested_data["comments"]) >= 10:
    #                 break

    #     return nested_data
        
    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": []
        }

        # Adding in 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("<br/>", "").replace("<span style='padding-left: 30px'></span>", "")
                    comment_text = re.sub(r'&[^;]+;', '', comment_text)

                    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', 'Anonymous').split(',')[0]).capitalize(),
                            "commenter_lname": (comment_data.get('lastName', 'Anonymous').split(',')[0]).capitalize(),
                            "comment_length": len(comment_text)
                        }
                        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"),
            "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
        )
    
    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