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reddit-wallstreetbets-aug-2021.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""The SocialGrep dataset loader base."""
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import csv
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import os
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import datasets
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DATASET_NAME = "wallstreetbets-posts-and-comments-for-august-2021"
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DATASET_TITLE = "wallstreetbets-posts-and-comments-for-august-2021"
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DATASET_DESCRIPTION = """\
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This corpus contains the complete data for the activity on /r/WallStreetBets for the entire month of August 2021.
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"""
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_HOMEPAGE = f"https://socialgrep.com/datasets/{DATASET_NAME}"
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+
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_LICENSE = "CC-BY v4.0"
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URL_TEMPLATE = "https://exports.socialgrep.com/download/public/{dataset_file}.zip"
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DATASET_FILE_TEMPLATE = "{dataset}-{type}.csv"
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_DATASET_FILES = {
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'posts': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="posts"),
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'comments': DATASET_FILE_TEMPLATE.format(dataset=DATASET_NAME, type="comments"),
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}
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_CITATION = f"""\
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@misc{{socialgrep:{DATASET_NAME},
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title = {{{DATASET_TITLE}}},
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author={{Lexyr Inc.
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}},
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year={{2022}}
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}}
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"""
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class redditwallstreetbetsaug2021(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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# If you need to make complex sub-parts in the datasets with configurable options
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# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="posts", version=VERSION, description="The dataset posts."),
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datasets.BuilderConfig(name="comments", version=VERSION, description="The dataset comments."),
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]
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def _info(self):
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if self.config.name == "posts": # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"type": datasets.Value("string"),
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"id": datasets.Value("string"),
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"subreddit.id": datasets.Value("string"),
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"subreddit.name": datasets.Value("string"),
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"subreddit.nsfw": datasets.Value("bool"),
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"created_utc": datasets.Value("timestamp[s,tz=utc]"),
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"permalink": datasets.Value("string"),
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"domain": datasets.Value("string"),
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"url": datasets.Value("string"),
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"selftext": datasets.Value("large_string"),
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"title": datasets.Value("string"),
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"score": datasets.Value("int32"),
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}
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)
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else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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features = datasets.Features(
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{
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"type": datasets.ClassLabel(num_classes=2, names=['post', 'comment']),
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"id": datasets.Value("string"),
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"subreddit.id": datasets.Value("string"),
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"subreddit.name": datasets.Value("string"),
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"subreddit.nsfw": datasets.Value("bool"),
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"created_utc": datasets.Value("timestamp[s,tz=utc]"),
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"permalink": datasets.Value("string"),
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"body": datasets.Value("large_string"),
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"sentiment": datasets.Value("float32"),
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"score": datasets.Value("int32"),
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=DATASET_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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my_urls = [URL_TEMPLATE.format(dataset_file=_DATASET_FILES[self.config.name])]
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data_dir = dl_manager.download_and_extract(my_urls)[0]
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, _DATASET_FILES[self.config.name]),
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"split": "train",
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},
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)
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]
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def _generate_examples(
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self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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):
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""" Yields examples as (key, example) tuples. """
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# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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bool_cols = ["subreddit.nsfw"]
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int_cols = ["score", "created_utc"]
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float_cols = ["sentiment"]
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for row in reader:
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for col in bool_cols:
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if col in row:
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if row[col]:
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row[col] = (row[col] == "true")
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else:
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row[col] = None
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for col in int_cols:
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if col in row:
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if row[col]:
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row[col] = int(row[col])
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else:
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row[col] = None
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for col in float_cols:
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if col in row:
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if row[col]:
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row[col] = float(row[col])
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else:
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row[col] = None
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+
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if row["type"] == "post":
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key = f"t3_{row['id']}"
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if row["type"] == "comment":
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key = f"t1_{row['id']}"
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yield key, row
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if __name__ == "__main__":
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print("Please use the HuggingFace dataset library, or")
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print("download from https://socialgrep.com/datasets.")
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