File size: 7,144 Bytes
80a0d7a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0c3f2ab
80a0d7a
 
 
 
 
 
7fd10e2
80a0d7a
7fd10e2
 
 
 
5c25b73
 
7fd10e2
 
 
 
 
 
 
 
 
 
 
 
80a0d7a
 
7fd10e2
80a0d7a
 
 
 
e0ee352
7fd10e2
f6e03bc
7fd10e2
e0ee352
7fd10e2
 
 
80a0d7a
08cb6c7
7fd10e2
 
 
80a0d7a
4e7e1de
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
553ebeb
4e7e1de
 
 
 
 
 
 
 
 
bf92d2d
4e7e1de
 
 
 
 
 
 
 
 
 
 
 
 
7fd10e2
553ebeb
7fd10e2
 
 
4e7e1de
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
# 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_mod.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"]
    #             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
    def _generate_examples(self, filepath):
        """Generates examples from a JSON file."""
        print("Generating examples...")
        with open(filepath, 'r', encoding='utf-8') as file:
            data = json.load(file)
            for key, docket in enumerate(data):
                docket_id = docket.get("id", f"missing_id_{key}")
                docket_title = docket.get("title", "No Title")
                docket_context = docket.get("context", "No Context")
                docket_purpose = docket.get("purpose", "Unspecified")
                docket_keywords = docket.get("keywords", [])

                # Process comments
                comments = docket.get("comments", [])

                # Extracting fields from each comment
                comment_texts = [comment.get("text", "").strip() for comment in comments]
                comment_ids = [comment.get("comment_id", "") for comment in comments]
                comment_urls = [comment.get("comment_url", "") for comment in comments]
                comment_dates = [comment.get("comment_date", "") for comment in comments]
                comment_titles = [comment.get("comment_title", "") for comment in comments]
                commenter_fnames = [comment.get("commenter_fname", "") for comment in comments]
                commenter_lnames = [comment.get("commenter_lname", "") for comment in comments]
                comment_lengths = [comment.get("comment_length", 0) for comment in comments]

                yield key, {
                    "id": docket_id,
                    "title": docket_title,
                    "context": docket_context,
                    "purpose": docket_purpose,
                    "keywords": docket_keywords,
                    "comments": {
                        "text": comment_texts,
                        "comment_id": comment_ids,
                        "comment_url": comment_urls,
                        "comment_date": comment_dates,
                        "comment_title": comment_titles,
                        "commenter_fname": commenter_fnames,
                        "commenter_lname": commenter_lnames,
                        "comment_length": comment_lengths
                    }
                }