regulatory_comments.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.
"""TODO: Add a description here."""


import json
import datasets


_DESCRIPTION = """\
United States governmental agencies often make proposed regulations open to the public for comment. 
This project will use Regulation.gov public API to aggregate and clean public comments for dockets 
related to Medication Assisted Treatment for Opioid Use Disorders. 

The dataset will contain docket metadata, docket text-content, comment metadata, and comment text-content. 
"""

_HOMEPAGE = "https://www.regulations.gov/"


# 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)
_URLS = {"url": "https://huggingface.co./datasets/ro-h/regulatory_comments/raw/main/docket_comments.json"}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class RegComments(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")
    
    def _info(self):
        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")
            })
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        print("split generators called")
        # URLS should point to where your dataset is located
        urls = _URLS["url"]
        data_dir = dl_manager.download_and_extract(urls)
        print("urls accessed")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir,
                },
            ),
        ]
    
    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        print("enter generate")
        key = 0
        with open(filepath, 'r', encoding='utf-8') as f:
            data = json.load(f)
            for docket in data:
                docket_id = docket["id"]
                docket_title = docket["title"]
                docket_context = docket["context"]
                print("did access")
                docket_purpose = docket.get("purpose", "unspecified")
                docket_keywords = docket.get("keywords", [])
                comments = docket["comments"]
                # for comment in docket["comments"]:
                #     comment_data = {
                #         "text": comment["text"],
                #         "comment_id": comment["comment_id"],
                #         "comment_url": comment["comment_url"],
                #         "comment_date": comment["comment_date"],
                #         "comment_title": comment["comment_title"],
                #         "commenter_fname": comment["commenter_fname"],
                #         "commenter_lname": comment["commenter_lname"],
                #         "comment_length": comment["comment_length"]
                #     }
                #     comments.append(comment_data)

                yield key, {
                    "id": docket_id,
                    "title": docket_title,
                    "context": docket_context,
                    "purpose": docket_purpose, 
                    "keywords": docket_keywords, 
                    "comments": comments
                }
                key += 1