<reponame>dkajtoch/datasets<filename>datasets/ar_cov19/ar_cov19.py
# 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.
"""TODO: Add a description here."""

from __future__ import absolute_import, division, print_function

import glob
import os

import pandas as pd

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{haouari2020arcov19,
  title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
  author={<NAME> and <NAME> and <NAME> and <NAME>},
  journal={arXiv preprint arXiv:2004.05861},
  year={2020}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://gitlab.com/bigirqu/ArCOV-19"

# TODO: Add the licence for the dataset here if you can find it
# _LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip"
# _URL="https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip?path=dataset/all_tweets"


class ArCov19Config(datasets.BuilderConfig):
    """BuilderConfig for ArCOV19."""

    def __init__(self, **kwargs):
        """BuilderConfig for ArCOV19.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ArCov19Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ArCov19(datasets.GeneratorBasedBuilder):
    """ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others"""

    VERSION = datasets.Version("1.1.0")

    # 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 = [
        ArCov19Config(
            name="ar_cov19",
            description="Plain text",
        )
    ]

    def _info(self):

        features = {}

        features["tweetID"] = datasets.Value("int64")

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.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."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # 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
        data_dir = dl_manager.download_and_extract(_URL)
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})]

    def _generate_examples(self, data_dir):
        """ Yields examples. """
        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
        # The key is not important, it's more here for legacy reason (legacy from tfds)
        for fname in sorted(glob.glob(os.path.join(data_dir, "ArCOV-19-master/dataset/all_tweets/2020-*"))):

            df = pd.read_csv(fname, names=["tweetID"])
            for id_, record in df.iterrows():

                tweetID = record["tweetID"]

                yield str(id_), {"tweetID": tweetID}

# 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.
"""TODO: Add a description here."""

from __future__ import absolute_import, division, print_function

import glob
import os

import pandas as pd

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{haouari2020arcov19,
  title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
  author={<NAME> and <NAME> and <NAME> and <NAME>},
  journal={arXiv preprint arXiv:2004.05861},
  year={2020}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://gitlab.com/bigirqu/ArCOV-19"

# TODO: Add the licence for the dataset here if you can find it
# _LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip"
# _URL="https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip?path=dataset/all_tweets"


class ArCov19Config(datasets.BuilderConfig):
    """BuilderConfig for ArCOV19."""

    def __init__(self, **kwargs):
        """BuilderConfig for ArCOV19.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ArCov19Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ArCov19(datasets.GeneratorBasedBuilder):
    """ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others"""

    VERSION = datasets.Version("1.1.0")

    # 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 = [
        ArCov19Config(
            name="ar_cov19",
            description="Plain text",
        )
    ]

    def _info(self):

        features = {}

        features["tweetID"] = datasets.Value("int64")

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.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."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # 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
        data_dir = dl_manager.download_and_extract(_URL)
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})]

    def _generate_examples(self, data_dir):
        """ Yields examples. """
        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
        # The key is not important, it's more here for legacy reason (legacy from tfds)
        for fname in sorted(glob.glob(os.path.join(data_dir, "ArCOV-19-master/dataset/all_tweets/2020-*"))):

            df = pd.read_csv(fname, names=["tweetID"])
            for id_, record in df.iterrows():

                tweetID = record["tweetID"]

                yield str(id_), {"tweetID": tweetID}
# 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. TODO: Add a description here. # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website \ @article{haouari2020arcov19,   title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},   author={<NAME> and <NAME> and <NAME> and <NAME>},   journal={arXiv preprint arXiv:2004.05861},   year={2020} # TODO: Add description of the dataset here # You can copy an official description \ ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others # TODO: Add a link to an official homepage for the dataset here # TODO: Add the licence for the dataset here if you can find it # _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) # _URL="https://gitlab.com/bigirqu/ArCOV-19/-/archive/master/ArCOV-19-master.zip?path=dataset/all_tweets" BuilderConfig for ArCOV19. BuilderConfig for ArCOV19.          Args:           **kwargs: keyword arguments forwarded to super. # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 30th of April 2020. ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing, among others # 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') # This is the description that will appear on the datasets page. # This defines the different columns of the dataset and their types # 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. # Homepage of the dataset for documentation # License for the dataset if available # license=_LICENSE, # Citation for the dataset Returns SplitGenerators. # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # 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 Yields examples. # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. # It is in charge of opening the given file and yielding (key, example) tuples from the dataset # The key is not important, it's more here for legacy reason (legacy from tfds)