# coding=utf-8 # 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. """The SocialGrep dataset loader base.""" import csv import os import datasets DATASET_NAME = "the-reddit-dataset-dataset" DATASET_TITLE = "the-reddit-dataset-dataset" DATASET_DESCRIPTION = """\ A meta dataset of Reddit's own /r/datasets community. """ _HOMEPAGE = f"https://socialgrep.com/datasets/{DATASET_NAME}" _LICENSE = "CC-BY v4.0" URL_TEMPLATE = "https://exports.socialgrep.com/download/public/{dataset_file}.zip" DATASET_FILE_TEMPLATE = "{dataset}-{type}.csv" _DATASET_FILES = { 'posts': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="posts"), 'comments': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="comments"), } _CITATION = f"""\ @misc{{socialgrep:{DATASET_NAME}, title = {{{DATASET_TITLE}}}, author={{Lexyr Inc. }}, year={{2022}} }} """ class FiveYearsOfAAPLOnReddit(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") # 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="posts", version=VERSION, description="The dataset posts."), datasets.BuilderConfig(name="comments", version=VERSION, description="The dataset comments."), ] def _info(self): if self.config.name == "posts": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "type": datasets.Value("string"), "id": datasets.Value("string"), "subreddit.id": datasets.Value("string"), "subreddit.name": datasets.Value("string"), "subreddit.nsfw": datasets.Value("bool"), "created_utc": datasets.Value("timestamp[s,tz=utc]"), "permalink": datasets.Value("string"), "domain": datasets.Value("string"), "url": datasets.Value("string"), "selftext": datasets.Value("large_string"), "title": datasets.Value("string"), "score": datasets.Value("int32"), } ) else: # This is an example to show how to have different features for "first_domain" and "second_domain" features = datasets.Features( { "type": datasets.ClassLabel(num_classes=2, names=['post', 'comment']), "id": datasets.Value("string"), "subreddit.id": datasets.Value("string"), "subreddit.name": datasets.Value("string"), "subreddit.nsfw": datasets.Value("bool"), "created_utc": datasets.Value("timestamp[s,tz=utc]"), "permalink": datasets.Value("string"), "body": datasets.Value("large_string"), "sentiment": datasets.Value("float32"), "score": datasets.Value("int32"), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=DATASET_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, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # 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): """Returns SplitGenerators.""" # 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 my_urls = [URL_TEMPLATE.format(dataset_file=_DATASET_FILES[self.config.name])] data_dir = dl_manager.download_and_extract(my_urls)[0] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir, _DATASET_FILES[self.config.name]), "split": "train", }, ) ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. bool_cols = ["subreddit.nsfw"] int_cols = ["score", "created_utc"] float_cols = ["sentiment"] with open(filepath, encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: for col in bool_cols: if col in row: if row[col]: row[col] = (row[col] == "true") else: row[col] = None for col in int_cols: if col in row: if row[col]: row[col] = int(row[col]) else: row[col] = None for col in float_cols: if col in row: if row[col]: row[col] = float(row[col]) else: row[col] = None if row["type"] == "post": key = f"t3_{row['id']}" if row["type"] == "comment": key = f"t1_{row['id']}" yield key, row