reddit_climate_comment.py

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)

        # 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 = []

            # Extract comment data
            comment_ids = group['CommentID'].tolist()
            comment_authors = group['CommentAuthor'].tolist()
            comment_bodies = group['CommentBody'].tolist()
            comment_timestamps = group['CommentTimestamp'].tolist()
            comment_upvotes = group['CommentUpvotes'].tolist()
            comment_permalinks = group['CommentPermalink'].tolist()
            replies = group['replies'].tolist()  # Assuming 'replies' column is a list

            for i in range(len(comment_ids)):
                comment_id = str(comment_ids[i])

                if pd.isna(comment_id):  # Skip if CommentID is NaN
                    continue

                comment = {
                    "CommentID": comment_id,
                    "CommentAuthor": comment_authors[i],
                    "CommentBody": comment_bodies[i],
                    "CommentTimestamp": comment_timestamps[i],
                    "CommentUpvotes": comment_upvotes[i],
                    "CommentPermalink": comment_permalinks[i],
                    "replies": []  # Initialize empty list for replies
                }

                # Check if 'replies' column exists and is not NaN
                if replies[i] is not None and not pd.isna(replies[i]):
                    # Filter out NaNs from reply IDs
                    reply_ids = [str(reply_id) for reply_id in replies[i] if not pd.isna(reply_id)]

                    # Add replies for the current comment
                    for reply_id in reply_ids:
                        reply_data = group[group['ReplyID'] == reply_id].iloc[0]
                        reply = {
                            "ReplyID": reply_id,
                            "ReplyAuthor": reply_data['ReplyAuthor'],
                            "ReplyBody": reply_data['ReplyBody'],
                            "ReplyTimestamp": reply_data['ReplyTimestamp'],
                            "ReplyUpvotes": reply_data['ReplyUpvotes'],
                            "ReplyPermalink": reply_data['ReplyPermalink']
                        }
                        comment["replies"].append(reply)

                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
            }