tmvar_v2.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 itertools
import os
from pydoc import doc
from typing import Dict, Iterator, List, Tuple

import datasets

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

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{wei2018tmvar,
title={tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine},
author={Wei, Chih-Hsuan and Phan, Lon and Feltz, Juliana and Maiti, Rama and Hefferon, Tim and Lu, Zhiyong},
journal={Bioinformatics},
volume={34},
number={1},
pages={80--87},
year={2018},
publisher={Oxford University Press}
}
"""

_DATASETNAME = "tmvar_v2"
_DISPLAYNAME = "tmVar v2"

_DESCRIPTION = """This dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.
It can be used for NER tasks and NED tasks, This dataset has a single split"""

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

_LICENSE = 'License information unavailable'

_URLS = {
    _DATASETNAME: "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/tmVar/tmVar.Normalization.txt",
}

_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]

_SOURCE_VERSION = "2.0.0"

_BIGBIO_VERSION = "1.0.0"

logger = datasets.utils.logging.get_logger(__name__)


class TmvarV2Dataset(datasets.GeneratorBasedBuilder):
    """
    This dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.
    """

    DEFAULT_CONFIG_NAME = "tmvar_v2_source"
    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = []
    BUILDER_CONFIGS.append(
        BigBioConfig(
            name=f"{_DATASETNAME}_source",
            version=SOURCE_VERSION,
            description=f"{_DATASETNAME} source schema",
            schema="source",
            subset_id=f"{_DATASETNAME}",
        )
    )
    BUILDER_CONFIGS.append(
        BigBioConfig(
            name=f"{_DATASETNAME}_bigbio_kb",
            version=BIGBIO_VERSION,
            description=f"{_DATASETNAME} BigBio schema",
            schema="bigbio_kb",
            subset_id=f"{_DATASETNAME}",
        )
    )

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "pmid": datasets.Value("string"),
                    "passages": [
                        {
                            "type": datasets.Value("string"),
                            "text": datasets.Value("string"),
                            "offsets": [datasets.Value("int32")],
                        }
                    ],
                    "entities": [
                        {
                            "text": datasets.Value("string"),
                            "offsets": [datasets.Value("int32")],
                            "concept_id": datasets.Value("string"),
                            "semantic_type_id": datasets.Value("string"),
                            "rsid": 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."""

        url = _URLS[_DATASETNAME]
        train_filepath = dl_manager.download(url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": train_filepath,
                },
            )
        ]

    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        if self.config.schema == "source":
            with open(filepath, "r", encoding="utf8") as fstream:
                for raw_document in self.generate_raw_docs(fstream):
                    document = self.parse_raw_doc(raw_document)
                    yield document["pmid"], document

        elif self.config.schema == "bigbio_kb":
            with open(filepath, "r", encoding="utf8") as fstream:
                uid = itertools.count(0)
                for raw_document in self.generate_raw_docs(fstream):
                    document = self.parse_raw_doc(raw_document)
                    document["id"] = next(uid)
                    document["document_id"] = document.pop("pmid")

                    entities_ = []
                    for entity in document["entities"]:
                        if entity.get("rsid", ""):
                            normalized = [
                                {
                                    "db_name": "dbsnp",
                                    "db_id": entity.get("rsid").split(":")[1],
                                }
                            ]
                        else:
                            normalized = []

                        entities_.append(
                            {
                                "id": next(uid),
                                "type": entity["semantic_type_id"],
                                "text": [entity["text"]],
                                "normalized": normalized,
                                "offsets": [entity["offsets"]],
                            }
                        )
                    for passage in document["passages"]:
                        passage["id"] = next(uid)

                    document["entities"] = entities_
                    document["relations"] = []
                    document["events"] = []
                    document["coreferences"] = []

                    yield document["document_id"], document

    def generate_raw_docs(self, fstream):
        """
        Given a filestream, this function yields documents from it
        """
        raw_document = []
        for line in fstream:
            if line.strip():
                raw_document.append(line.strip())
            elif raw_document:
                yield raw_document
                raw_document = []
        if raw_document:
            yield raw_document

    def parse_raw_doc(self, raw_doc):
        pmid, _, title = raw_doc[0].split("|")
        pmid = int(pmid)
        _, _, abstract = raw_doc[1].split("|")

        if self.config.schema == "source":
            passages = [
                {"type": "title", "text": title, "offsets": [0, len(title)]},
                {
                    "type": "abstract",
                    "text": abstract,
                    "offsets": [len(title) + 1, len(title) + len(abstract) + 1],
                },
            ]
        elif self.config.schema == "bigbio_kb":
            passages = [
                {"type": "title", "text": [title], "offsets": [[0, len(title)]]},
                {
                    "type": "abstract",
                    "text": [abstract],
                    "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]],
                },
            ]

        entities = []
        for count, line in enumerate(raw_doc[2:]):
            line_pieces = line.split("\t")
            if len(line_pieces) == 6:
                if pmid == 18166824 and count == 0:
                    # this example has the following text
                    # 18166824    880    948    amino acid (proline) with a polar amino acid (serine) at position 29    p|SUB|P|29|S    RSID:2075789
                    # it is missing the semantic_type_id between `... position 29` and `p|SUB|P|29|S`
                    pmid_ = str(pmid)
                    start_idx = "880"
                    end_idx = "948"
                    mention = "amino acid (proline) with a polar amino acid (serine) at position 29"
                    semantic_type_id = "ProteinMutation"
                    entity_id = "p|SUB|P|29|S"
                    rsid = "RSID:2075789"
                    assert line_pieces[0] == pmid_
                    assert line_pieces[1] == start_idx
                    assert line_pieces[2] == end_idx
                    assert line_pieces[3] == mention
                    assert line_pieces[4] == entity_id
                    assert line_pieces[5] == rsid
                    logger.info(
                        f"Adding ProteinMutation semantic_type_id in Document ID: {pmid} Line: {line}"
                    )
                else:
                    (
                        pmid_,
                        start_idx,
                        end_idx,
                        mention,
                        semantic_type_id,
                        entity_id,
                    ) = line_pieces
                    rsid = None

            elif len(line_pieces) == 7:
                (
                    pmid_,
                    start_idx,
                    end_idx,
                    mention,
                    semantic_type_id,
                    entity_id,
                    rsid,
                ) = line_pieces

            else:
                logger.info(
                    f"Inconsistent entity format found. Skipping Document ID: {pmid} Line: {line}"
                )
                continue

            entity = {
                "offsets": [int(start_idx), int(end_idx)],
                "text": mention,
                "semantic_type_id": semantic_type_id,
                "concept_id": entity_id,
                "rsid": rsid,
            }
            entities.append(entity)

        return {"pmid": pmid, "passages": passages, "entities": entities}