Datasets:
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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
}
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