Datasets:

bigbio
/

hallmarks_of_cancer

	# coding=utf-8
	# Copyright 2022 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.
	from pathlib import Path

	import datasets

	from .bigbiohub import text_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = False
	_CITATION = """\
	@article{DBLP:journals/bioinformatics/BakerSGAHSK16,
	author = {Simon Baker and
	Ilona Silins and
	Yufan Guo and
	Imran Ali and
	Johan H{\"{o}}gberg and
	Ulla Stenius and
	Anna Korhonen},
	title = {Automatic semantic classification of scientific literature
	according to the hallmarks of cancer},
	journal = {Bioinform.},
	volume = {32},
	number = {3},
	pages = {432--440},
	year = {2016},
	url = {https://doi.org/10.1093/bioinformatics/btv585},
	doi = {10.1093/bioinformatics/btv585},
	timestamp = {Thu, 14 Oct 2021 08:57:44 +0200},
	biburl = {https://dblp.org/rec/journals/bioinformatics/BakerSGAHSK16.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DATASETNAME = "hallmarks_of_cancer"
	_DISPLAYNAME = "Hallmarks of Cancer"

	_DESCRIPTION = """\
	The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
	abstracts manually annotated by experts according to a taxonomy. The taxonomy
	consists of 37 classes in a hierarchy. Zero or more class labels are assigned
	to each sentence in the corpus. The labels are found under the "labels"
	directory, while the tokenized text can be found under "text" directory.
	The filenames are the corresponding PubMed IDs (PMID).
	"""

	_HOMEPAGE = "https://github.com/sb895/Hallmarks-of-Cancer"

	_LICENSE = 'GNU General Public License v3.0 only'

	_URLs = {
	"corpus": "https://github.com/sb895/Hallmarks-of-Cancer/archive/refs/heads/master.zip",
	"split_indices": "https://microsoft.github.io/BLURB/sample_code/data_generation.tar.gz",
	}

	_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
	_SOURCE_VERSION = "1.0.0"
	_BIGBIO_VERSION = "1.0.0"

	_CLASS_NAMES = [
	"evading growth suppressors",
	"tumor promoting inflammation",
	"enabling replicative immortality",
	"cellular energetics",
	"resisting cell death",
	"activating invasion and metastasis",
	"genomic instability and mutation",
	"none",
	"inducing angiogenesis",
	"sustaining proliferative signaling",
	"avoiding immune destruction",
	]


	class HallmarksOfCancerDataset(datasets.GeneratorBasedBuilder):
	"""Hallmarks Of Cancer Dataset"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="hallmarks_of_cancer_source",
	version=SOURCE_VERSION,
	description="Hallmarks of Cancer source schema",
	schema="source",
	subset_id="hallmarks_of_cancer",
	),
	BigBioConfig(
	name="hallmarks_of_cancer_bigbio_text",
	version=BIGBIO_VERSION,
	description="Hallmarks of Cancer Biomedical schema",
	schema="bigbio_text",
	subset_id="hallmarks_of_cancer",
	),
	]
	DEFAULT_CONFIG_NAME = "hallmarks_of_cancer_source"

	def _info(self):

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"document_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"label": datasets.Sequence(datasets.ClassLabel(names=_CLASS_NAMES)),
	}
	)

	elif self.config.schema == "bigbio_text":
	features = text_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"corpuspath": Path(data_dir["corpus"]),
	"indicespath": Path(data_dir["split_indices"])
	/ "data_generation/indexing/HoC/train_pmid.tsv",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"corpuspath": Path(data_dir["corpus"]),
	"indicespath": Path(data_dir["split_indices"])
	/ "data_generation/indexing/HoC/test_pmid.tsv",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"corpuspath": Path(data_dir["corpus"]),
	"indicespath": Path(data_dir["split_indices"])
	/ "data_generation/indexing/HoC/dev_pmid.tsv",
	},
	),
	]

	def _generate_examples(self, corpuspath: Path, indicespath: Path):

	indices = indicespath.read_text(encoding="utf8").strip("\n").split(",")
	dataset_dir = corpuspath / "Hallmarks-of-Cancer-master"
	texts_dir = dataset_dir / "text"
	labels_dir = dataset_dir / "labels"

	uid = 1
	for document_index, document in enumerate(indices):
	text_file = texts_dir / document
	label_file = labels_dir / document
	text = text_file.read_text(encoding="utf8").strip("\n")
	labels = label_file.read_text(encoding="utf8").strip("\n")

	sentences = text.split("\n")
	labels = labels.split("<")[1:]

	for example_index, example_pair in enumerate(zip(sentences, labels)):
	sentence, label = example_pair

	label = label.strip()

	if label == "":
	label = "none"

	multi_labels = [m_label.strip() for m_label in label.split("AND")]
	unique_multi_labels = {
	m_label.split("--")[0].lower().lstrip()
	for m_label in multi_labels
	if m_label != "NULL"
	}

	arrow_file_unique_key = 100 * document_index + example_index
	if self.config.schema == "source":
	yield arrow_file_unique_key, {
	"document_id": f"{text_file.name.split('.')[0]}_{example_index}",
	"text": sentence,
	"label": list(unique_multi_labels),
	}
	elif self.config.schema == "bigbio_text":
	yield arrow_file_unique_key, {
	"id": uid,
	"document_id": f"{text_file.name.split('.')[0]}_{example_index}",
	"text": sentence,
	"labels": list(unique_multi_labels),
	}
	uid += 1