Upload regulatory_comments.py

regulatory_comments.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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+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 = "https://huggingface.co/datasets/ro-h/regulatory_comments/blob/main/temp.csv"
+
+
+# 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")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+
+    #my_dataset[comment_id] = dict(
+    #            comment_url = comment['links']['self'], 
+    #            comment_text = comment_data['data']['attributes']['comment'],
+    #            commenter_name = comment_data['data']['attributes'].get('firstName', '') + " " + comment_data['data']['attributes'].get('lastName', '')
+    #        ) #use pandas
+
+    def _info(self):
+        features = datasets.Features(
+              {"docket_agency": datasets.Value("string"),
+               "docket_title": datasets.Value("string"),
+               "comment_id": datasets.Value("string"),
+               "comment_date": datasets.Value("date"),
+               "comment_link": datasets.Value("string"),
+               "comment_title": datasets.Value("string"),
+               "commenter_name": datasets.Value("string"),
+               "comment_length": datasets.Value("int"),
+               "comment_text": datasets.Value("string"),
+              }
+          )
+        
+        
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.csv"),
+                    "split": "train",
+                },),]
+    
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for key, row in enumerate(reader):
+                yield key, {
+                    "docket_agency": row["docket_agency"],
+                    "docket_title": row["docket_title"],
+                    "comment_id": row["comment_id"],
+                    "comment_date": row["comment_date"],
+                    "comment_link": row["comment_link"],
+                    "comment_title": row["comment_title"],
+                    "commenter_name": row["commenter_name"],
+                    "comment_length": int(row["comment_length"]),
+                    "comment_text": row["comment_text"],
+                }