Create new file

reddit-nonewnormal-complete.py

@@ -0,0 +1,182 @@
+# 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 = "reddit-r-nonewnormal-dataset"
+DATASET_TITLE = "reddit-r-nonewnormal-dataset"
+
+DATASET_DESCRIPTION = """\
+This corpus contains the complete data for the activity on subreddit /r/NoNewNormal for the entire duration of its existence.
+"""
+
+_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 redditnonewnormalcomplete(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.")