Delete loading script

guardian_authorship.py

@@ -1,325 +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.
-"""This is an authorship attribution dataset based on the work of Stamatatos 2013. """
-
-
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{article,
-    author = {Stamatatos, Efstathios},
-    year = {2013},
-    month = {01},
-    pages = {421-439},
-    title = {On the robustness of authorship attribution based on character n-gram features},
-    volume = {21},
-    journal = {Journal of Law and Policy}
-}
-
-@inproceedings{stamatatos2017authorship,
-    title={Authorship attribution using text distortion},
-    author={Stamatatos, Efstathios},
-    booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
-    volume={1}
-    pages={1138--1149},
-    year={2017}
-}
-"""
-
-_DESCRIPTION = """\
-A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
-1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
-2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
-
-3- The same-topic/genre scenario is created by grouping all the datasts as follows.
-For ex., to use same_topic and split the data 60-40 use:
-train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
-                        split='train[:60%]+validation[:60%]+test[:60%]')
-tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
-                        split='train[-40%:]+validation[-40%:]+test[-40%:]')
-
-IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced
-
-* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples
-"""
-
-_URL = "https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1"
-
-
-# Using a specific configuration class is optional, you can also use the base class if you don't need
-# to add specific attributes.
-# here we give an example for three sub-set of the dataset with difference sizes.
-class GuardianAuthorshipConfig(datasets.BuilderConfig):
-    """BuilderConfig for NewDataset"""
-
-    def __init__(self, train_folder, valid_folder, test_folder, **kwargs):
-        """
-        Args:
-            Train_folder: Topic/genre used for training
-            valid_folder:       ~      ~   for validation
-            test_folder:        ~      ~   for testing
-
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(GuardianAuthorshipConfig, self).__init__(**kwargs)
-        self.train_folder = train_folder
-        self.valid_folder = valid_folder
-        self.test_folder = test_folder
-
-
-class GuardianAuthorship(datasets.GeneratorBasedBuilder):
-    """dataset for same- and cross-topic authorship attribution"""
-
-    config_counter = 0
-    BUILDER_CONFIG_CLASS = GuardianAuthorshipConfig
-    BUILDER_CONFIGS = [
-        # cross-topic
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{1}",
-            version=datasets.Version(f"{1}.0.0", description=f"The Original DS with the cross-topic scenario no.{1}"),
-            train_folder="Politics",
-            valid_folder="Society",
-            test_folder="UK,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{2}",
-            version=datasets.Version(f"{2}.0.0", description=f"The Original DS with the cross-topic scenario no.{2}"),
-            train_folder="Politics",
-            valid_folder="UK",
-            test_folder="Society,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{3}",
-            version=datasets.Version(f"{3}.0.0", description=f"The Original DS with the cross-topic scenario no.{3}"),
-            train_folder="Politics",
-            valid_folder="World",
-            test_folder="Society,UK",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{4}",
-            version=datasets.Version(f"{4}.0.0", description=f"The Original DS with the cross-topic scenario no.{4}"),
-            train_folder="Society",
-            valid_folder="Politics",
-            test_folder="UK,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{5}",
-            version=datasets.Version(f"{5}.0.0", description=f"The Original DS with the cross-topic scenario no.{5}"),
-            train_folder="Society",
-            valid_folder="UK",
-            test_folder="Politics,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{6}",
-            version=datasets.Version(f"{6}.0.0", description=f"The Original DS with the cross-topic scenario no.{6}"),
-            train_folder="Society",
-            valid_folder="World",
-            test_folder="Politics,UK",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{7}",
-            version=datasets.Version(f"{7}.0.0", description=f"The Original DS with the cross-topic scenario no.{7}"),
-            train_folder="UK",
-            valid_folder="Politics",
-            test_folder="Society,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{8}",
-            version=datasets.Version(f"{8}.0.0", description=f"The Original DS with the cross-topic scenario no.{8}"),
-            train_folder="UK",
-            valid_folder="Society",
-            test_folder="Politics,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{9}",
-            version=datasets.Version(f"{9}.0.0", description=f"The Original DS with the cross-topic scenario no.{9}"),
-            train_folder="UK",
-            valid_folder="World",
-            test_folder="Politics,Society",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{10}",
-            version=datasets.Version(
-                f"{10}.0.0", description=f"The Original DS with the cross-topic scenario no.{10}"
-            ),
-            train_folder="World",
-            valid_folder="Politics",
-            test_folder="Society,UK",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{11}",
-            version=datasets.Version(
-                f"{11}.0.0", description=f"The Original DS with the cross-topic scenario no.{11}"
-            ),
-            train_folder="World",
-            valid_folder="Society",
-            test_folder="Politics,UK",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_topic_{12}",
-            version=datasets.Version(
-                f"{12}.0.0", description=f"The Original DS with the cross-topic scenario no.{12}"
-            ),
-            train_folder="World",
-            valid_folder="UK",
-            test_folder="Politics,Society",
-        ),
-        # # cross-genre
-        GuardianAuthorshipConfig(
-            name=f"cross_genre_{1}",
-            version=datasets.Version(f"{1}.0.0", description=f"The Original DS with the cross-genre scenario no.{1}"),
-            train_folder="Books",
-            valid_folder="Politics",
-            test_folder="Society,UK,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_genre_{2}",
-            version=datasets.Version(f"{2}.0.0", description=f"The Original DS with the cross-genre scenario no.{2}"),
-            train_folder="Books",
-            valid_folder="Society",
-            test_folder="Politics,UK,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_genre_{3}",
-            version=datasets.Version(f"{3}.0.0", description=f"The Original DS with the cross-genre scenario no.{3}"),
-            train_folder="Books",
-            valid_folder="UK",
-            test_folder="Politics,Society,World",
-        ),
-        GuardianAuthorshipConfig(
-            name=f"cross_genre_{4}",
-            version=datasets.Version(f"{4}.0.0", description=f"The Original DS with the cross-genre scenario no.{4}"),
-            train_folder="Books",
-            valid_folder="World",
-            test_folder="Politics,Society,UK",
-        ),
-    ]
-
-    def _info(self):
-        # Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    # There are 13 authors in this dataset
-                    "author": datasets.features.ClassLabel(
-                        names=[
-                            "catherinebennett",
-                            "georgemonbiot",
-                            "hugoyoung",
-                            "jonathanfreedland",
-                            "martinkettle",
-                            "maryriddell",
-                            "nickcohen",
-                            "peterpreston",
-                            "pollytoynbee",
-                            "royhattersley",
-                            "simonhoggart",
-                            "willhutton",
-                            "zoewilliams",
-                        ]
-                    ),
-                    # There are book reviews, and articles on the following four topics
-                    "topic": datasets.features.ClassLabel(names=["Politics", "Society", "UK", "World", "Books"]),
-                    "article": datasets.Value("string"),
-                }
-            ),
-            # 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=[("article", "author")],
-            # Homepage of the dataset for documentation
-            homepage="http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-
-        # This folder contains the orginal/2013 dataset
-        data_dir = os.path.join(dl_dir, "Guardian", "Guardian_original")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.train_folder, "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.test_folder, "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.valid_folder, "split": "valid"},
-            ),
-        ]
-
-    def _generate_examples(self, data_dir, samples_folders, split):
-        """Yields examples."""
-        # Yields (key, example) tuples from the dataset
-
-        # Training and validation are on 1 topic/genre, while testing is on multiple topics
-        # We convert the sample folders into list (from string)
-        if samples_folders.count(",") == 0:
-            samples_folders = [samples_folders]
-        else:
-            samples_folders = samples_folders.split(",")
-
-        # the dataset is structured as:
-        # |-Topic1
-        # |---author 1
-        # |------- article-1
-        # |------- article-2
-        # |---author 2
-        # |------- article-1
-        # |------- article-2
-        # |-Topic2
-        # ...
-
-        for topic in samples_folders:
-            full_path = os.path.join(data_dir, topic)
-
-            for author in os.listdir(full_path):
-
-                list_articles = os.listdir(os.path.join(full_path, author))
-                if len(list_articles) == 0:
-                    # Some authors have no articles on certain topics
-                    continue
-
-                for id_, article in enumerate(list_articles):
-                    path_2_author = os.path.join(full_path, author)
-                    path_2_article = os.path.join(path_2_author, article)
-
-                    with open(path_2_article, "r", encoding="utf8", errors="ignore") as f:
-                        art = f.readlines()
-
-                    # The whole article is stored as one line. We access the 1st element of the list
-                    # to store it as string, not as a list
-                    yield f"{topic}_{author}_{id_}", {
-                        "article": art[0],
-                        "author": author,
-                        "topic": topic,
-                    }