citation_gia_test_collection.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
#
# 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 os
from typing import List

import datasets
import xml.etree.ElementTree as ET
import uuid
import html

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

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{Wei2015,
  title        = {
    {GNormPlus}: An Integrative Approach for Tagging Genes,  Gene Families,
    and Protein Domains
  },
  author       = {Chih-Hsuan Wei and Hung-Yu Kao and Zhiyong Lu},
  year         = 2015,
  journal      = {{BioMed} Research International},
  publisher    = {Hindawi Limited},
  volume       = 2015,
  pages        = {1--7},
  doi          = {10.1155/2015/918710},
  url          = {https://doi.org/10.1155/2015/918710}
}
"""

_DATASETNAME = "citation_gia_test_collection"
_DISPLAYNAME = "Citation GIA Test Collection"

_DESCRIPTION = """\
The Citation GIA Test Collection was recently created for gene indexing at the
NLM and includes 151 PubMed abstracts with both mention-level and document-level
annotations. They are selected because both have a focus on human genes.
"""

_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/"

_LICENSE = 'License information unavailable'

_URLS = {
    _DATASETNAME: [
        "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/GNormPlus/GNormPlusCorpus.zip"
    ]
}

_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class CitationGIATestCollection(datasets.GeneratorBasedBuilder):
    """
    The Citation GIA Test Collection was recently created for gene indexing at the
    NLM and includes 151 PubMed abstracts with both mention-level and document-level
    annotations. They are selected because both have a focus on human genes.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="citation_gia_test_collection_source",
            version=SOURCE_VERSION,
            description="citation_gia_test_collection source schema",
            schema="source",
            subset_id="citation_gia_test_collection",
        ),
        BigBioConfig(
            name="citation_gia_test_collection_bigbio_kb",
            version=BIGBIO_VERSION,
            description="citation_gia_test_collection BigBio schema",
            schema="bigbio_kb",
            subset_id="citation_gia_test_collection",
        ),
    ]

    DEFAULT_CONFIG_NAME = "citation_gia_test_collection_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "passages": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                        }
                    ],
                    "entities": [
                        {
                            "id": datasets.Value("string"),
                            "type": datasets.Value("string"),
                            "text": datasets.Sequence(datasets.Value("string")),
                            "offsets": datasets.Sequence([datasets.Value("int32")]),
                            "normalized": [
                                {
                                    "db_name": datasets.Value("string"),
                                    "db_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) -> List[datasets.SplitGenerator]:

        urls = _URLS[_DATASETNAME]
        data_dir = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir[0], "GNormPlusCorpus/NLMIAT.BioC.xml"
                    ),
                    "split": "NLMIAT",
                },
            ),
        ]

    def _get_entities(self, annot_d: dict) -> dict:
        """'
        Converts annotation dict to entity dict.
        """
        ent = {
            "id": str(uuid.uuid4()),
            "type": annot_d["type"],
            "text": [annot_d["text"]],
            "offsets": [annot_d["offsets"]],
            "normalized": [
                {
                    "db_name": "NCBI Gene" if annot_d["type"].isdigit() else "",
                    "db_id": annot_d["type"] if annot_d["type"].isdigit() else "",
                }
            ],
        }

        return ent

    def _get_offsets_entities(
        child, parent_text: str, child_text: str, offset: int
    ) -> List[int]:
        """
        Extracts child text offsets from parent text for entities.
        Some offsets that were present in the datset were wrong mainly because of string encodings.
        Also a little fraction of parent strings doesn't contain its respective child strings.
        Hence few assertion errors in the entitity offsets checking test.
        """
        if child_text in parent_text:
            index = parent_text.index(child_text)
            start = index + offset

        else:
            start = offset
        end = start + len(child_text)

        return [start, end]

    def _process_annot(self, annot: ET.Element, passages: dict) -> dict:
        """'
        Converts annotation XML Element to Python dict.
        """
        parent_text = " ".join([p["text"] for p in passages.values()])
        annot_d = dict()
        a_d = {a.tag: a.text for a in annot}

        for a in list(annot):

            if a.tag == "location":
                offset = int(a.attrib["offset"])
                annot_d["offsets"] = self._get_offsets_entities(
                    html.escape(parent_text[offset:]), html.escape(a_d["text"]), offset
                )

            elif a.tag != "infon":
                annot_d[a.tag] = html.escape(a.text)

            else:
                annot_d[a.attrib["key"]] = html.escape(a.text)

        return annot_d

    def _parse_elem(self, elem: ET.Element) -> dict:
        """'
        Converts document XML Element to Python dict.
        """
        elem_d = dict()
        passages = dict()
        annotations = elem.findall(".//annotation")
        elem_d["entities"] = []

        for child in elem:
            elem_d[child.tag] = []

        for child in elem:
            if child.tag == "passage":
                elem_d[child.tag].append(
                    {
                        c.tag: html.escape(
                            " ".join(
                                list(
                                    filter(
                                        lambda item: item,
                                        [t.strip("\n") for t in c.itertext()],
                                    )
                                )
                            )
                        )
                        for c in child
                    }
                )

            elif child.tag == "id":
                elem_d[child.tag] = html.escape(child.text)

        for passage in elem_d["passage"]:
            infon = passage["infon"]
            passage.pop("infon", None)
            passages[infon] = passage

        elem_d["passages"] = passages
        elem_d.pop("passage", None)

        for a in annotations:
            elem_d["entities"].append(self._process_annot(a, elem_d["passages"]))

        return elem_d

    def _generate_examples(self, filepath, split):

        root = ET.parse(filepath).getroot()

        if self.config.schema == "source":
            uid = 0
            for elem in root.findall("document"):
                row = self._parse_elem(elem)
                uid += 1
                passages = row["passages"]
                yield uid, {
                    "id": str(uid),
                    "passages": [
                        {
                            "id": str(uuid.uuid4()),
                            "type": "title",
                            "text": [passages["title"]["text"]],
                            "offsets": [
                                [
                                    int(passages["title"]["offset"]),
                                    int(passages["title"]["offset"])
                                    + len(passages["title"]["text"]),
                                ]
                            ],
                        },
                        {
                            "id": str(uuid.uuid4()),
                            "type": "abstract",
                            "text": [passages["abstract"]["text"]],
                            "offsets": [
                                [
                                    int(passages["abstract"]["offset"]),
                                    int(passages["abstract"]["offset"])
                                    + len(passages["abstract"]["text"]),
                                ]
                            ],
                        },
                    ],
                    "entities": [self._get_entities(a) for a in row["entities"]],
                }

        elif self.config.schema == "bigbio_kb":
            uid = 0
            for elem in root.findall("document"):
                row = self._parse_elem(elem)
                uid += 1
                passages = row["passages"]
                yield uid, {
                    "id": str(uid),
                    "document_id": str(uuid.uuid4()),
                    "passages": [
                        {
                            "id": str(uuid.uuid4()),
                            "type": "title",
                            "text": [passages["title"]["text"]],
                            "offsets": [
                                [
                                    int(passages["title"]["offset"]),
                                    int(passages["title"]["offset"])
                                    + len(passages["title"]["text"]),
                                ]
                            ],
                        },
                        {
                            "id": str(uuid.uuid4()),
                            "type": "abstract",
                            "text": [passages["abstract"]["text"]],
                            "offsets": [
                                [
                                    int(passages["abstract"]["offset"]),
                                    int(passages["abstract"]["offset"])
                                    + len(passages["abstract"]["text"]),
                                ]
                            ],
                        },
                    ],
                    "entities": [self._get_entities(a) for a in row["entities"]],
                    "relations": [],
                    "events": [],
                    "coreferences": [],
                }