Datasets:

steamcyclone
/

Pill_Ideologies-Post_Titles

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# TODO: Address all TODOs and remove all explanatory comments
	"""This script's purpose is to re-define the dataset loading functions to better suit this specific
	reddit posts dataset."""

	import csv
	import json
	import os
	import pandas as pd
	from sklearn.model_selection import train_test_split

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {Pill Ideologies Subreddits Dataset},
	author={Eric Rios},
	year={2024}
	source = {reddit.com}
	}

	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to aid research in the ongoing study of the pill ideologies subreddits,
	which have risen in response to the clashes between traditional gender roles and the rise of fourth wave feminism.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "cc" # cc-by-4.0

	# 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 = {
	"first_domain": "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles/resolve/main/reddit_posts_fm.csv",
	"second_domain": "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles/resolve/main/reddit_posts_fm.csv",
	}


	# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
	class SubRedditPosts(datasets.GeneratorBasedBuilder):
	"""This dataset contains data from the pill ideologies subreddits and the feminism subreddit.

	It has the subreddit,post_id, title, text, url, score, author, and date.

	It was fully scraped for new subreddits on March 19th, 2024."""

	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="first_domain",
	version=VERSION,
	description="This part of my dataset covers a first domain",
	),
	datasets.BuilderConfig(
	name="second_domain",
	version=VERSION,
	description="This part of my dataset covers a second domain",
	),
	]

	DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if (
	self.config.name == "first_domain"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"subreddit": datasets.Value("string"),
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	"url": datasets.Value("string"),
	"score": datasets.Value("int64"),
	# "author": datasets.Value("string"), removed for privacy reasons
	"date": datasets.Value("float64"),
	"subreddit_subscribers": datasets.Value("int64"),
	"num_comments": datasets.Value("int64"),
	"ups": datasets.Value("int64"),
	"downs": datasets.Value("int64"),
	"upvote_ratio": datasets.Value("float64"),
	"is_video": datasets.Value("bool"),
	# These are the features of your dataset like images, labels ...
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"subreddit": datasets.Value("string"),
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	"url": datasets.Value("string"),
	"score": datasets.Value("int64"),
	# "author": datasets.Value("string"), removed for privacy reasons
	"date": datasets.Value("float64"),
	"subreddit_subscribers": datasets.Value("int64"),
	"num_comments": datasets.Value("int64"),
	"ups": datasets.Value("int64"),
	"downs": datasets.Value("int64"),
	"upvote_ratio": datasets.Value("float64"),
	"is_video": datasets.Value("bool"),
	# These are the features of your dataset like images, labels ...
	}
	)
	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
	urls = _URLS[self.config.name]
	data_dir = dl_manager.download_and_extract(urls)
	data = pd.read_csv(data_dir)
	data = self.process_data(data)

	# commented out the splits, due to google colab being uncooperative
	# raised too many errors that my local machine did not,

	# make splits
	train, test = train_test_split(
	data, test_size=0.10, stratify=data["subreddit"], random_state=42
	)
	train, val = train_test_split(
	train, test_size=0.20, stratify=train["subreddit"], random_state=42
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	# "filepath": train,
	"filepath": train,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": val,
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": test,
	"split": "test",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: 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.

	for key, row in filepath.iterrows():

	if self.config.name == "first_domain":
	yield key, {
	"subreddit": row.get("subreddit"),
	"id": row.get("id"),
	"title": row.get("title"),
	"text": row.get("text"),
	"url": row.get("url"),
	"score": row.get("score"),
	# "author": row.get("author"), removed for privacy reasons
	"date": row.get("date"),
	"subreddit_subscribers": row.get("subreddit_subscribers"),
	"num_comments": row.get("num_comments"),
	"ups": row.get("ups"),
	"downs": row.get("downs"),
	"upvote_ratio": row.get("upvote_ratio"),
	"is_video": row.get("is_video"),
	}

	else:
	yield key, {
	"subreddit": row.get("subreddit"),
	"id": row.get("id"),
	"title": row.get("title"),
	"text": row.get("text"),
	"url": row.get("url"),
	"score": row.get("score"),
	# "author": row.get("author"), removed for privacy reasons
	"date": row.get("date"),
	"subreddit_subscribers": row.get("subreddit_subscribers"),
	"num_comments": row.get("num_comments"),
	"ups": row.get("ups"),
	"downs": row.get("downs"),
	"upvote_ratio": row.get("upvote_ratio"),
	"is_video": row.get("is_video"),
	}

	def process_data(self, df):
	"""This function takes a dataframe and processes it to remove any unwanted columns and rows"""
	# columns that cannot be empty,so drop rows
	df = df.dropna(subset=["subreddit"])
	df = df.dropna(subset=["title"])

	# cleaning to make colab importing the dataset through huggingface work
	values = {
	"id": "",
	"text": "",
	"url": "",
	"score": 0,
	"date": 0.0,
	"subreddit_subscribers": 0,
	"num_comments": 0,
	"ups": 0,
	"downs": 0,
	"upvote_ratio": 0.0,
	"is_video": False,
	}

	df.fillna(value=values, inplace=True)

	return df