Delete reddit-wallstreetbets-aug-2021.py

reddit-wallstreetbets-aug-2021.py

@@ -1,182 +0,0 @@
-# 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 = "wallstreetbets-posts-and-comments-for-august-2021"
-DATASET_TITLE = "wallstreetbets-posts-and-comments-for-august-2021"
-
-DATASET_DESCRIPTION = """\
-This corpus contains the complete data for the activity on /r/WallStreetBets for the entire month of August 2021.
-"""
-
-_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 redditwallstreetbetsaug2021(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
-
-
-if __name__ == "__main__":
-    print("Please use the HuggingFace dataset library, or")
-    print("download from https://socialgrep.com/datasets.")