import csv import json import os from datasets import GeneratorBasedBuilder, Features, Value, Sequence, SplitGenerator, BuilderConfig, DatasetInfo, Split import logging import pandas as pd from typing import Dict CITATION = "" _DESCRIPTION = "Demo" _URL = "" _HOMEPAGE = "" _LICENSE = "" _URL = "https://github.com/catherine-ywang/reddit_climate_comment_data/raw/main/climate_comments.csv.zip" class NewDataset(GeneratorBasedBuilder): def _info(self): return DatasetInfo( description=_DESCRIPTION, features=Features({ "id": Value("string"), "post_title": Value("string"), "post_author": Value("string"), "post_body": Value("string"), "post_url": Value("string"), "post_pic": Value("string"), "subreddit": Value("string"), "post_timestamp": Value("string"), "post_upvotes": Value("int32"), "post_permalink": Value("string"), "comments": Sequence({ "CommentID": Value("string"), "CommentAuthor": Value("string"), "CommentBody": Value("string"), "CommentTimestamp": Value("string"), "CommentUpvotes": Value("int32"), "CommentPermalink": Value("string"), "replies": Sequence({ "ReplyID": Value("string"), "ReplyAuthor": Value("string"), "ReplyBody": Value("string"), "ReplyTimestamp": Value("string"), "ReplyUpvotes": Value("int32"), "ReplyPermalink": Value("string"), }) }) }), homepage=_HOMEPAGE, ) def _split_generators(self, dl_manager): path = dl_manager.download_and_extract(_URL) train_splits = SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": path+"/climate_comments.csv"}) return [train_splits] def _generate_examples(self, filepath): df = pd.read_csv(filepath) for column in df.columns: df[column] = df[column].replace({pd.NA: None}) # Group the DataFrame by post ID grouped_df = df.groupby('PostID') for post_id, group in grouped_df: post_data = group.iloc[0] # Get the data for the post post_title = post_data['PostTitle'] post_author = post_data['PostAuthor'] post_body = post_data['PostBody'] post_url = post_data['PostUrl'] post_pic = post_data['PostPic'] subreddit = post_data['Subreddit'] post_timestamp = post_data['PostTimestamp'] post_upvotes = post_data['PostUpvotes'] post_permalink = post_data['PostPermalink'] comments = [] # Iterate over each unique comment ID for comment_id in group['CommentID'].unique(): comment_data = group[group['CommentID'] == comment_id].iloc[0] comment_author = comment_data['CommentAuthor'] comment_body = comment_data['CommentBody'] comment_timestamp = comment_data['CommentTimestamp'] comment_upvotes = comment_data['CommentUpvotes'] comment_permalink = comment_data['CommentPermalink'] # Get all replies for the current comment replies = [] reply_group = df[df['CommentID'] == comment_id] for _, reply_data in reply_group.iterrows(): reply_id = reply_data['ReplyID'] reply_author = reply_data['ReplyAuthor'] reply_body = reply_data['ReplyBody'] reply_timestamp = reply_data['ReplyTimestamp'] reply_upvotes = reply_data['ReplyUpvotes'] reply_permalink = reply_data['ReplyPermalink'] reply = { "ReplyID": reply_id, "ReplyAuthor": reply_author, "ReplyBody": reply_body, "ReplyTimestamp": reply_timestamp, "ReplyUpvotes": reply_upvotes, "ReplyPermalink": reply_permalink } replies.append(reply) # Add comment with its replies to the list comment = { "CommentID": comment_id, "CommentAuthor": comment_author, "CommentBody": comment_body, "CommentTimestamp": comment_timestamp, "CommentUpvotes": comment_upvotes, "CommentPermalink": comment_permalink, "replies": replies } comments.append(comment) yield post_id, { "id": post_id, "post_title": post_title, "post_author": post_author, "post_body": post_body, "post_url": post_url, "post_pic": post_pic, "subreddit": subreddit, "post_timestamp": post_timestamp, "post_upvotes": post_upvotes, "post_permalink": post_permalink, "comments": comments }