reddit_climate_data.py

"""TODO: Add a description here."""


import csv
import json
import os
import logging


import datasets


# 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/reddit_climate_comment"
}

# 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:
            data = json.load(f)
            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
                }