raft.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.

import csv
import json
import os

import datasets

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# You can copy an official description
_DESCRIPTION = """
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# 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)
# This gets all folders within the directory named `data`
DATA_DIRS = next(os.walk('data'))[1]

_URLs = {s: {'train': f"data/{s}/train.csv",
             'test': f"data/{s}/test_unlabeled.csv"} for s in DATA_DIRS}


class Raft(datasets.GeneratorBasedBuilder):
    """RAFT Dataset."""

    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')

    # TODO: Load task jsons

    tasks = {}
    for sd in DATA_DIRS:
        with open(os.path.join('data', sd, 'task.json')) as f:
            task_data = json.load(f)
        tasks[sd] = task_data

    BUILDER_CONFIGS = []
    for key in tasks:
        td = tasks[key]
        name = td['name']
        description = td['description']
        BUILDER_CONFIGS.append(datasets.BuilderConfig(name=name, version=VERSION,
                                                      description=description))

    DEFAULT_CONFIG_NAME = "tai_safety_research"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        DEFAULT_LABEL_NAME = "Unlabeled"

        task = Raft.tasks[self.config.name]
        data_columns = {col_name: datasets.Value("string") for col_name in
                        task['data_columns']}

        label_columns = {}
        for label_name in task['label_columns']:
            labels = [DEFAULT_LABEL_NAME] + task['label_columns'][label_name]
            label_columns[label_name] = datasets.ClassLabel(len(labels), labels)

        # Merge dicts
        features = datasets.Features(**data_columns, **label_columns)

        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=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(_URLs)
        dataset = self.config.name
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                    gen_kwargs={"filepath": data_dir[dataset]['train'],
                                                "split": "train"}),
            datasets.SplitGenerator(name=datasets.Split.TEST,
                                    gen_kwargs={"filepath": data_dir[dataset]['test'],
                                                "split": "test"})
        ]

    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.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        task = Raft.tasks[self.config.name]
        labels = list(task['label_columns'])

        with open(filepath, encoding="utf-8") as f:
            csv_reader = csv.reader(f, quotechar='"', delimiter=",",
                                    quoting=csv.QUOTE_ALL, skipinitialspace=True)
            column_names = next(csv_reader)
            # Test csvs don't have any label columns.
            if split == "test":
                column_names += labels

            for id_, row in enumerate(csv_reader):
                if split == "test":
                    row += ["Unlabeled"] * len(labels)
                # dicts don't have inherent ordering in python, right??
                yield id_, {name: value for name, value in zip(column_names, row)}