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genia_relation_corpus

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genia_relation_corpus / genia_relation_corpus.py

gabrielaltay

fix bigbio imports

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	# 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.

	"""
	The extraction of various relations stated to hold between biomolecular entities is one of the most frequently
	addressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein
	interactions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the
	state or properties of biomolecules are captured in the event annotation.

	The GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily)
	static relations, relations such as part-of that hold between entities without (necessarily) involving change.
	"""

	import os
	from pathlib import Path
	from typing import Dict, List, Tuple

	import datasets

	from .bigbiohub import kb_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks
	from .bigbiohub import parse_brat_file
	from .bigbiohub import brat_parse_to_bigbio_kb


	_LANGUAGES = ['English']
	_PUBMED = True
	_LOCAL = False
	_CITATION = """\
	@inproceedings{pyysalo-etal-2009-static,
	title = "Static Relations: a Piece in the Biomedical Information Extraction Puzzle",
	author = "Pyysalo, Sampo and
	Ohta, Tomoko and
	Kim, Jin-Dong and
	Tsujii, Jun{'}ichi",
	booktitle = "Proceedings of the {B}io{NLP} 2009 Workshop",
	month = jun,
	year = "2009",
	address = "Boulder, Colorado",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/W09-1301",
	pages = "1--9",
	}

	@article{article,
	author = {Ohta, Tomoko and Pyysalo, Sampo and Kim, Jin-Dong and Tsujii, Jun'ichi},
	year = {2010},
	month = {10},
	pages = {917-28},
	title = {A reevaluation of biomedical named entity - term relations},
	volume = {8},
	journal = {Journal of bioinformatics and computational biology},
	doi = {10.1142/S0219720010005014}
	}

	@MISC{Hoehndorf_applyingontology,
	author = {Robert Hoehndorf and Axel-cyrille Ngonga Ngomo and Sampo Pyysalo and Tomoko Ohta and Anika Oellrich and
	Dietrich Rebholz-schuhmann},
	title = {Applying ontology design patterns to the implementation of relations in GENIA},
	year = {}
	}
	"""

	_DATASETNAME = "genia_relation_corpus"
	_DISPLAYNAME = "GENIA Relation Corpus"

	_DESCRIPTION = """\
	The extraction of various relations stated to hold between biomolecular entities is one of the most frequently
	addressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein
	interactions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the
	state or properties of biomolecules are captured in the event annotation.

	The GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily)
	static relations, relations such as part-of that hold between entities without (necessarily) involving change.
	"""

	_HOMEPAGE = "http://www.geniaproject.org/genia-corpus/relation-corpus"

	_LICENSE = 'GENIA Project License for Annotated Corpora'

	_URLS = {
	_DATASETNAME: {
	"train": "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Relation/GENIA_relation_annotation_training_data.tar.gz",
	"validation": "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Relation/GENIA_relation_annotation_development_data.tar.gz",
	"test": "http://www.nactem.ac.uk/GENIA/current/GENIA-corpus/Relation/GENIA_relation_annotation_test_data.tar.gz",
	},
	}

	_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION]

	_SOURCE_VERSION = "1.0.0"

	_BIGBIO_VERSION = "1.0.0"


	class GeniaRelationCorpusDataset(datasets.GeneratorBasedBuilder):
	"""GENIA Relation corpus."""

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

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="genia_relation_corpus_source",
	version=SOURCE_VERSION,
	description="genia_relation_corpus source schema",
	schema="source",
	subset_id="genia_relation_corpus",
	),
	BigBioConfig(
	name="genia_relation_corpus_bigbio_kb",
	version=BIGBIO_VERSION,
	description="genia_relation_corpus BigBio schema",
	schema="bigbio_kb",
	subset_id="genia_relation_corpus",
	),
	]

	DEFAULT_CONFIG_NAME = "genia_relation_corpus_source"

	def _info(self) -> datasets.DatasetInfo:
	if self.config.schema == "source":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"document_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
	{
	"offsets": datasets.Sequence([datasets.Value("int32")]),
	"text": datasets.Sequence(datasets.Value("string")),
	"type": datasets.Value("string"),
	"id": datasets.Value("string"),
	}
	],
	"relations": [ # R line in brat
	{
	"id": datasets.Value("string"),
	"head": {
	"ref_id": datasets.Value("string"),
	"role": datasets.Value("string"),
	},
	"tail": {
	"ref_id": datasets.Value("string"),
	"role": datasets.Value("string"),
	},
	"type": datasets.Value("string"),
	}
	],
	"equivalences": [ # Equiv line in brat
	{
	"id": datasets.Value("string"),
	"ref_ids": datasets.Sequence(datasets.Value("string")),
	}
	],
	},
	)

	elif self.config.schema == "bigbio_kb":
	features = kb_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
	"""Returns SplitGenerators."""
	urls = _URLS[_DATASETNAME]
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"data_dir": data_dir[split],
	},
	)
	for split in [
	datasets.Split.TRAIN,
	datasets.Split.VALIDATION,
	datasets.Split.TEST,
	]
	]

	def _generate_examples(self, data_dir) -> Tuple[int, Dict]:
	"""Yields examples as (key, example) tuples."""
	for dirpath, _, filenames in os.walk(data_dir):
	for guid, filename in enumerate(filenames):
	if filename.endswith(".txt"):
	txt_file_path = Path(dirpath, filename)
	if self.config.schema == "source":
	example = parse_brat_file(
	txt_file_path, annotation_file_suffixes=[".a1", ".rel"]
	)
	example["id"] = str(guid)
	for key in ["events", "attributes", "normalizations"]:
	del example[key]
	yield guid, example
	elif self.config.schema == "bigbio_kb":
	example = brat_parse_to_bigbio_kb(
	parse_brat_file(txt_file_path)
	)
	example["id"] = str(guid)
	yield guid, example