Datasets:

ICILS
/

isco_esco_occupations_taxonomy

	from datasets.utils import file_utils
	from pathlib import Path
	import json
	import os
	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """TBA"""

	_DESCRIPTION = """\
	ISCO ESCO Occupations Taxonomy Dataset (IEOTD) is a hierarhical \
	taxonomy dataset, consisting of occupation groups from ISCO, \
	occupations from ESCO and definitions.
	"""

	# Define the path to the labels directory
	_LABELS_DIR = Path(__file__).parent / "labels"

	# Check if the labels directory exists, and create it if it doesn't
	if not _LABELS_DIR.exists():
	_LABELS_DIR.mkdir()

	_LICENSE = """\
	By accessing ISCO ESCO Occupations Taxonomy Dataset, you indicate that you agree to the terms and conditions associated with their use. Please read the IEA Disclaimer and License Agreement for full details. [Disclaimer_and_License_Agreement.pdf (iea.nl)](https://www.iea.nl/sites/default/files/data-repository/Disclaimer_and_License_Agreement.pdf)
	"""

	_HOMEPAGE = "https://iea.nl"

	_URL = "./data"
	_LABELS_URL = str(_LABELS_DIR / "isco_codes.txt")
	# Alternative option is https://huggingface.co/datasets/ICILS/isco_esco_occupations_taxonomy/raw/main/labels/isco_codes.txt
	_URLS = {
	"isco_taxonomy": _URL + "/isco_taxonomy.jsonl",
	"isco_occupations": _URL + "/isco_occupations.jsonl",
	"isco_labels": _LABELS_URL,
	}


	class IscoTaxonomyConfig(datasets.BuilderConfig):
	"""BuilderConfig for ISCO ESCO Taxonomy."""

	def __init__(self, **kwargs):
	"""BuilderConfig for SQUAD.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(IscoTaxonomyConfig, self).__init__(**kwargs)


	class IscoTaxonomy(datasets.GeneratorBasedBuilder):
	"""The ISCO ESCO Occupations Taxonomy Dataset v1.0.0"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="isco_taxonomy",
	version=datasets.Version("1.0.0", ""),
	description="ISCO groups and definitions, and ESCO occupations and definitions.",
	),
	datasets.BuilderConfig(
	name="isco_occupations",
	version=datasets.Version("1.0.0", ""),
	description="ISCO occupations index.",
	),
	]

	BUILDER_CONFIG_CLASS = IscoTaxonomyConfig
	DEFAULT_CONFIG_NAME = "isco_taxonomy"

	def _info(self):
	if (
	self.config.name == "isco_taxonomy"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"text": datasets.features.Value("string"),
	"labels": datasets.features.ClassLabel(
	names_file=os.path.join(_LABELS_URL)
	),
	}
	)
	elif self.config.name == "isco_occupations": # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"text": datasets.features.Value("string"),
	"labels": datasets.features.ClassLabel(
	names_file=os.path.join(_LABELS_URL)
	),
	# These are the features of your dataset like images, labels ...
	}
	)
	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, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	supervised_keys=("text", "labels"),
	# 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):
	# 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
	urls = _URLS[self.config.name]
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	"split": "isco_taxonomy",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	"split": "isco_occupations",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "isco_taxonomy":
	# Yields examples as (key, example) tuples
	yield key, {
	"text": data["ISCO_DEFINITION_1"],
	"labels": "" if split == "test" else data["ISCO_CODE_1"],
	}
	elif self.config.name == "isco_occupations":
	yield key, {
	"text": data["ISCO_OCCUPATION"],
	"labels": "" if split == "test" else data["ISCO_CODE"],
	}