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climate_data.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93fc0e5aacc47d0206cb2dac3dcf627494c4817171c63db8e300ee8cc1a3388c
+size 16723047

comment_data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4a15f498476cf271def33ae786f72ea7926e3bd1634aff6f7d166c9bfa94b0f
+size 17846138

csvtransformerjson.py

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+import csv
+import pandas as pd
+
+class CSVtoJSONTransformer:
+ def __init__(self, csv_reader):
+ self.csv_reader = csv_reader
+ 
+ def transform_to_json(self):
+ df = pd.DataFrame(self.csv_reader) # Convert csv_reader to DataFrame
+ df["Author"] = df["Author"].replace({pd.NA: None})
+ df["Comment Body"] = df["Comment Body"].replace({pd.NA: None})
+
+ # Define an empty list to store the transformed data
+ transformed_data = []
+
+ # Group the DataFrame by the 'Subreddit' column
+ grouped_df = df.groupby('Subreddit')
+
+ # Iterate through each subreddit group
+ for subreddit, group in grouped_df:
+ subreddit_data = {
+ "Subreddit": subreddit,
+ "Posts": []
+ }
+ 
+ # Iterate through each row in the subreddit group
+ for index, row in group.iterrows():
+ post_title = row['Post Title']
+ comment_body = row['Comment Body']
+ timestamp = row['Timestamp']
+ upvotes = row['Upvotes']
+ ID = row['ID']
+ author = row['Author']
+ replies = row['Number of Replies']
+ 
+ # Check if the post title already exists under the subreddit
+ post_exists = False
+ for post in subreddit_data["Posts"]:
+ if post['PostTitle'] == post_title:
+ post_exists = True
+ post['Comments'].append({
+ "CommentID": ID,
+ "Author": author,
+ "CommentBody": comment_body,
+ "Timestamp": timestamp,
+ "Upvotes": upvotes,
+ "NumberofReplies": replies
+ })
+ break
+ 
+ # If the post title does not exist, create a new entry
+ if not post_exists:
+ subreddit_data["Posts"].append({
+ "PostID": len(subreddit_data["Posts"]) + 1,
+ "PostTitle": post_title,
+ "Comments": [{
+ "CommentID": ID,
+ "Author": author,
+ "CommentBody": comment_body,
+ "Timestamp": timestamp,
+ "Upvotes": upvotes,
+ "NumberofReplies": replies
+ }]
+ })
+ 
+ # Append the subreddit data to the transformed data list
+ transformed_data.append(subreddit_data)
+
+ return transformed_data

reddit_climate_data.py

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+
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+import logging
+import datasets
+from csvtransformerjson import CSVtoJSONTransformer
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2024}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# 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 = {
+ "reddit_climate": "cathw/comment_data"
+}
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+ """TODO: Short description of my dataset."""
+
+ 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')
+ BUILDER_CONFIGS = [
+ datasets.BuilderConfig(name="reddit_climate", version=VERSION, description="This part of my dataset covers a first domain")
+ ]
+
+ DEFAULT_CONFIG_NAME = "reddit_climate" # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+ def _info(self):
+ 
+ features = datasets.Features({
+ "Subreddit": datasets.Value("string"),
+ "Posts": datasets.Sequence({
+ "PostID": datasets.Value("int32"),
+ "PostTitle": datasets.Value("string"),
+ "Comments": datasets.Sequence({
+ "CommentID": datasets.Value("string"),
+ "Author": datasets.Value("string"),
+ "CommentBody": datasets.Value("string"),
+ "Timestamp": datasets.Value("string"),
+ "Upvotes": datasets.Value("int32"),
+ "NumberofReplies": datasets.Value("int32"),
+ }),
+ }),
+ })
+ 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,
+ # License for the dataset if available
+ license=_LICENSE,
+ # Citation for the dataset
+ citation=_CITATION,
+ )
+
+ 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
+ config_name = getattr(self.config, 'name', self.DEFAULT_CONFIG_NAME)
+ urls = _URLS.get(config_name, {}) # Get the URLs for the configuration name, if not found, return an empty dictionary
+ 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": data_dir,
+ "split": "train",
+ },
+ ),
+ ]
+
+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+ def _generate_examples(self, filepath, split):
+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+ # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+ with open(filepath, encoding="utf-8") as f:
+ csv_reader = csv.reader(f)
+ data = CSVtoJSONTransformer(csv_reader)
+ for idx, row in enumerate(data):
+ subreddit = row["Subreddit"]
+ posts = []
+ # Check if the "Posts" key is present in the current row
+ if "Posts" in row:
+ for post in row["Posts"]:
+ post_id = post["PostID"]
+ post_title = post["PostTitle"]
+ comments = []
+ for comment in post["Comments"]:
+ comment_id = comment["CommentID"]
+ author = comment["Author"]
+ comment_body = comment["CommentBody"]
+ timestamp = comment["Timestamp"]
+ upvotes = comment["Upvotes"]
+ number_of_replies = comment["NumberofReplies"]
+ comments.append({
+ "CommentID": comment_id,
+ "Author": author,
+ "CommentBody": comment_body,
+ "Timestamp": timestamp,
+ "Upvotes": upvotes,
+ "NumberofReplies": number_of_replies
+ })
+ posts.append({
+ "PostID": post_id,
+ "PostTitle": post_title,
+ "Comments": comments
+ })
+ else:
+ # Handle cases where the "Posts" key is missing
+ posts = None
+ 
+ yield idx, {
+ "Subreddit": subreddit,
+ "Posts": posts
+ }