pcr.py

# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
A corpus for plant and chemical entities and for the relationships between them.
The corpus contains 2218 plant and chemical entities and 600 plant-chemical
relationships which are drawn from 1109 sentences in 245 PubMed abstracts.
"""
from pathlib import Path
from typing import Dict, Iterator, Tuple

import datasets

from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{choi2016corpus,
  title        = {A corpus for plant-chemical relationships in the biomedical domain},
  author       = {
    Choi, Wonjun and Kim, Baeksoo and Cho, Hyejin and Lee, Doheon and Lee,
    Hyunju
  },
  year         = 2016,
  journal      = {BMC bioinformatics},
  publisher    = {Springer},
  volume       = 17,
  number       = 1,
  pages        = {1--15}
}
"""

_DATASETNAME = "pcr"
_DISPLAYNAME = "PCR"

_DESCRIPTION = """
A corpus for plant / herb and chemical entities and for the relationships \
between them. The corpus contains 2218 plant and chemical entities and 600 \
plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed \
abstracts.
"""

_HOMEPAGE = "http://210.107.182.73/plantchemcorpus.htm"
_LICENSE = 'License information unavailable'

_URLS = {_DATASETNAME: "http://210.107.182.73/1109_corpus_units_STformat.tar"}

_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.EVENT_EXTRACTION]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class PCRDataset(datasets.GeneratorBasedBuilder):
    """
    The corpus of plant-chemical relation consists of plants / herbs and
    chemicals and relations between them.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="pcr_source",
            version=SOURCE_VERSION,
            description="PCR source schema",
            schema="source",
            subset_id="pcr",
        ),
        BigBioConfig(
            name="pcr_fixed_source",
            version=SOURCE_VERSION,
            description="PCR (with fixed offsets) source schema",
            schema="source",
            subset_id="pcr_fixed",
        ),
        BigBioConfig(
            name="pcr_bigbio_kb",
            version=BIGBIO_VERSION,
            description="PCR BigBio schema",
            schema="bigbio_kb",
            subset_id="pcr",
        ),
    ]

    DEFAULT_CONFIG_NAME = "pcr_source"

    def _info(self):
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "document_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "entities": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "normalized": [
                                {
                                    "db_name": datasets.Value("string"),
                                    "db_id": datasets.Value("string"),
                                }
                            ],
                        }
                    ],
                    "events": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            # refers to the text_bound_annotation of the trigger
                            "trigger": {
                                "text": datasets.Sequence(datasets.Value("string")),
                                "offsets": datasets.Sequence([datasets.Value("int32")]),
                            },
                            "arguments": [
                                {
                                    "role": datasets.Value("string"),
                                    "ref_id": 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):
        urls = _URLS[_DATASETNAME]
        data_dir = Path(dl_manager.download_and_extract(urls))
        data_dir = data_dir / "1109 corpus units"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_dir": data_dir},
            )
        ]

    def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
        if self.config.schema == "source":
            for file in data_dir.iterdir():
                if not str(file).endswith(".txt"):
                    continue

                example = parsing.parse_brat_file(file)
                example = parsing.brat_parse_to_bigbio_kb(example)
                example = self._to_source_example(example)

                # Three documents have incorrect offsets - fix them for fixed_source scheme
                if self.config.subset_id == "pcr_fixed" and example["document_id"] in [
                    "463",
                    "509",
                    "566",
                ]:
                    example = self._fix_example(example)

                yield example["document_id"], example

        elif self.config.schema == "bigbio_kb":
            for file in data_dir.iterdir():
                if not str(file).endswith(".txt"):
                    continue

                example = parsing.parse_brat_file(file)
                example = parsing.brat_parse_to_bigbio_kb(example)

                document_id = example["document_id"]
                example["id"] = document_id

                # Three documents have incorrect offsets - fix them for BigBio scheme
                if document_id in ["463", "509", "566"]:
                    example = self._fix_example(example)

                yield example["id"], example

    def _to_source_example(self, bigbio_example: Dict) -> Dict:
        """
        Converts an example in BigBio-KB scheme to an example according to the source scheme
        """
        source_example = bigbio_example.copy()
        source_example["text"] = bigbio_example["passages"][0]["text"][0]

        source_example.pop("passages", None)
        source_example.pop("relations", None)
        source_example.pop("coreferences", None)

        return source_example

    def _fix_example(self, example: Dict) -> Dict:
        """
        Fixes by the example by adapting the offsets of the trigger word of the first
        event. In the official annotation data the end offset is incorrect (for 3 examples).
        """
        first_event = example["events"][0]
        trigger_text = first_event["trigger"]["text"][0]
        offsets = first_event["trigger"]["offsets"][0]

        real_offsets = [offsets[0], offsets[0] + len(trigger_text)]
        example["events"][0]["trigger"]["offsets"] = [real_offsets]

        return example