bigbiohub.py

from collections import defaultdict
from dataclasses import dataclass
from enum import Enum
import logging
from pathlib import Path
from types import SimpleNamespace
from typing import Dict, Iterable, List, Tuple

import bioc
import datasets


logger = logging.getLogger(__name__)


BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")


@dataclass
class BigBioConfig(datasets.BuilderConfig):
    """BuilderConfig for BigBio."""

    name: str = None
    version: datasets.Version = None
    description: str = None
    schema: str = None
    subset_id: str = None


class Tasks(Enum):
    NAMED_ENTITY_RECOGNITION = "NER"
    NAMED_ENTITY_DISAMBIGUATION = "NED"
    EVENT_EXTRACTION = "EE"
    RELATION_EXTRACTION = "RE"
    COREFERENCE_RESOLUTION = "COREF"
    QUESTION_ANSWERING = "QA"
    TEXTUAL_ENTAILMENT = "TE"
    SEMANTIC_SIMILARITY = "STS"
    TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
    PARAPHRASING = "PARA"
    TRANSLATION = "TRANSL"
    SUMMARIZATION = "SUM"
    TEXT_CLASSIFICATION = "TXTCLASS"


entailment_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "premise": datasets.Value("string"),
        "hypothesis": datasets.Value("string"),
        "label": datasets.Value("string"),
    }
)

pairs_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text_1": datasets.Value("string"),
        "text_2": datasets.Value("string"),
        "label": datasets.Value("string"),
    }
)

qa_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "question_id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "question": datasets.Value("string"),
        "type": datasets.Value("string"),
        "choices": [datasets.Value("string")],
        "context": datasets.Value("string"),
        "answer": datasets.Sequence(datasets.Value("string")),
    }
)

text_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text": datasets.Value("string"),
        "labels": [datasets.Value("string")],
    }
)

text2text_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_id": datasets.Value("string"),
        "text_1": datasets.Value("string"),
        "text_2": datasets.Value("string"),
        "text_1_name": datasets.Value("string"),
        "text_2_name": datasets.Value("string"),
    }
)

kb_features = datasets.Features(
    {
        "id": datasets.Value("string"),
        "document_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"),
                    }
                ],
            }
        ],
        "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"),
                    }
                ],
            }
        ],
        "coreferences": [
            {
                "id": datasets.Value("string"),
                "entity_ids": datasets.Sequence(datasets.Value("string")),
            }
        ],
        "relations": [
            {
                "id": datasets.Value("string"),
                "type": datasets.Value("string"),
                "arg1_id": datasets.Value("string"),
                "arg2_id": datasets.Value("string"),
                "normalized": [
                    {
                        "db_name": datasets.Value("string"),
                        "db_id": datasets.Value("string"),
                    }
                ],
            }
        ],
    }
)


def get_texts_and_offsets_from_bioc_ann(ann: bioc.BioCAnnotation) -> Tuple:

    offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]

    text = ann.text

    if len(offsets) > 1:
        i = 0
        texts = []
        for start, end in offsets:
            chunk_len = end - start
            texts.append(text[i : chunk_len + i])
            i += chunk_len
            while i < len(text) and text[i] == " ":
                i += 1
    else:
        texts = [text]

    return offsets, texts


def remove_prefix(a: str, prefix: str) -> str:
    if a.startswith(prefix):
        a = a[len(prefix) :]
    return a


def parse_brat_file(
    txt_file: Path,
    annotation_file_suffixes: List[str] = None,
    parse_notes: bool = False,
) -> Dict:
    """
    Parse a brat file into the schema defined below.
    `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
    Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
    e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
    Will include annotator notes, when `parse_notes == True`.
    brat_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"),
                }
            ],
            "events": [  # E line in brat
                {
                    "trigger": datasets.Value(
                        "string"
                    ),  # refers to the text_bound_annotation of the trigger,
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "arguments": datasets.Sequence(
                        {
                            "role": datasets.Value("string"),
                            "ref_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")),
                }
            ],
            "attributes": [  # M or A lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "value": datasets.Value("string"),
                }
            ],
            "normalizations": [  # N lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "resource_name": datasets.Value(
                        "string"
                    ),  # Name of the resource, e.g. "Wikipedia"
                    "cuid": datasets.Value(
                        "string"
                    ),  # ID in the resource, e.g. 534366
                    "text": datasets.Value(
                        "string"
                    ),  # Human readable description/name of the entity, e.g. "Barack Obama"
                }
            ],
            ### OPTIONAL: Only included when `parse_notes == True`
            "notes": [  # # lines in brat
                {
                    "id": datasets.Value("string"),
                    "type": datasets.Value("string"),
                    "ref_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                }
            ],
        },
        )
    """

    example = {}
    example["document_id"] = txt_file.with_suffix("").name
    with txt_file.open() as f:
        example["text"] = f.read()

    # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
    # for event extraction
    if annotation_file_suffixes is None:
        annotation_file_suffixes = [".a1", ".a2", ".ann"]

    if len(annotation_file_suffixes) == 0:
        raise AssertionError(
            "At least one suffix for the to-be-read annotation files should be given!"
        )

    ann_lines = []
    for suffix in annotation_file_suffixes:
        annotation_file = txt_file.with_suffix(suffix)
        if annotation_file.exists():
            with annotation_file.open() as f:
                ann_lines.extend(f.readlines())

    example["text_bound_annotations"] = []
    example["events"] = []
    example["relations"] = []
    example["equivalences"] = []
    example["attributes"] = []
    example["normalizations"] = []

    if parse_notes:
        example["notes"] = []

    for line in ann_lines:
        line = line.strip()
        if not line:
            continue

        if line.startswith("T"):  # Text bound
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["type"] = fields[1].split()[0]
            ann["offsets"] = []
            span_str = remove_prefix(fields[1], (ann["type"] + " "))
            text = fields[2]
            for span in span_str.split(";"):
                start, end = span.split()
                ann["offsets"].append([int(start), int(end)])

            # Heuristically split text of discontiguous entities into chunks
            ann["text"] = []
            if len(ann["offsets"]) > 1:
                i = 0
                for start, end in ann["offsets"]:
                    chunk_len = end - start
                    ann["text"].append(text[i : chunk_len + i])
                    i += chunk_len
                    while i < len(text) and text[i] == " ":
                        i += 1
            else:
                ann["text"] = [text]

            example["text_bound_annotations"].append(ann)

        elif line.startswith("E"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]

            ann["type"], ann["trigger"] = fields[1].split()[0].split(":")

            ann["arguments"] = []
            for role_ref_id in fields[1].split()[1:]:
                argument = {
                    "role": (role_ref_id.split(":"))[0],
                    "ref_id": (role_ref_id.split(":"))[1],
                }
                ann["arguments"].append(argument)

            example["events"].append(ann)

        elif line.startswith("R"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["type"] = fields[1].split()[0]

            ann["head"] = {
                "role": fields[1].split()[1].split(":")[0],
                "ref_id": fields[1].split()[1].split(":")[1],
            }
            ann["tail"] = {
                "role": fields[1].split()[2].split(":")[0],
                "ref_id": fields[1].split()[2].split(":")[1],
            }

            example["relations"].append(ann)

        # '*' seems to be the legacy way to mark equivalences,
        # but I couldn't find any info on the current way
        # this might have to be adapted dependent on the brat version
        # of the annotation
        elif line.startswith("*"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["ref_ids"] = fields[1].split()[1:]

            example["equivalences"].append(ann)

        elif line.startswith("A") or line.startswith("M"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]

            info = fields[1].split()
            ann["type"] = info[0]
            ann["ref_id"] = info[1]

            if len(info) > 2:
                ann["value"] = info[2]
            else:
                ann["value"] = ""

            example["attributes"].append(ann)

        elif line.startswith("N"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["text"] = fields[2]

            info = fields[1].split()

            ann["type"] = info[0]
            ann["ref_id"] = info[1]
            ann["resource_name"] = info[2].split(":")[0]
            ann["cuid"] = info[2].split(":")[1]
            example["normalizations"].append(ann)

        elif parse_notes and line.startswith("#"):
            ann = {}
            fields = line.split("\t")

            ann["id"] = fields[0]
            ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL

            info = fields[1].split()

            ann["type"] = info[0]
            ann["ref_id"] = info[1]
            example["notes"].append(ann)

    return example


def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
    """
    Transform a brat parse (conforming to the standard brat schema) obtained with
    `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
    :param brat_parse:
    """

    unified_example = {}

    # Prefix all ids with document id to ensure global uniqueness,
    # because brat ids are only unique within their document
    id_prefix = brat_parse["document_id"] + "_"

    # identical
    unified_example["document_id"] = brat_parse["document_id"]
    unified_example["passages"] = [
        {
            "id": id_prefix + "_text",
            "type": "abstract",
            "text": [brat_parse["text"]],
            "offsets": [[0, len(brat_parse["text"])]],
        }
    ]

    # get normalizations
    ref_id_to_normalizations = defaultdict(list)
    for normalization in brat_parse["normalizations"]:
        ref_id_to_normalizations[normalization["ref_id"]].append(
            {
                "db_name": normalization["resource_name"],
                "db_id": normalization["cuid"],
            }
        )

    # separate entities and event triggers
    unified_example["events"] = []
    non_event_ann = brat_parse["text_bound_annotations"].copy()
    for event in brat_parse["events"]:
        event = event.copy()
        event["id"] = id_prefix + event["id"]
        trigger = next(
            tr
            for tr in brat_parse["text_bound_annotations"]
            if tr["id"] == event["trigger"]
        )
        if trigger in non_event_ann:
            non_event_ann.remove(trigger)
        event["trigger"] = {
            "text": trigger["text"].copy(),
            "offsets": trigger["offsets"].copy(),
        }
        for argument in event["arguments"]:
            argument["ref_id"] = id_prefix + argument["ref_id"]

        unified_example["events"].append(event)

    unified_example["entities"] = []
    anno_ids = [ref_id["id"] for ref_id in non_event_ann]
    for ann in non_event_ann:
        entity_ann = ann.copy()
        entity_ann["id"] = id_prefix + entity_ann["id"]
        entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
        unified_example["entities"].append(entity_ann)

    # massage relations
    unified_example["relations"] = []
    skipped_relations = set()
    for ann in brat_parse["relations"]:
        if (
            ann["head"]["ref_id"] not in anno_ids
            or ann["tail"]["ref_id"] not in anno_ids
        ):
            skipped_relations.add(ann["id"])
            continue
        unified_example["relations"].append(
            {
                "arg1_id": id_prefix + ann["head"]["ref_id"],
                "arg2_id": id_prefix + ann["tail"]["ref_id"],
                "id": id_prefix + ann["id"],
                "type": ann["type"],
                "normalized": [],
            }
        )
    if len(skipped_relations) > 0:
        example_id = brat_parse["document_id"]
        logger.info(
            f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
            f" Skip (for now): "
            f"{list(skipped_relations)}"
        )

    # get coreferences
    unified_example["coreferences"] = []
    for i, ann in enumerate(brat_parse["equivalences"], start=1):
        is_entity_cluster = True
        for ref_id in ann["ref_ids"]:
            if not ref_id.startswith("T"):  # not textbound -> no entity
                is_entity_cluster = False
            elif ref_id not in anno_ids:  # event trigger -> no entity
                is_entity_cluster = False
        if is_entity_cluster:
            entity_ids = [id_prefix + i for i in ann["ref_ids"]]
            unified_example["coreferences"].append(
                {"id": id_prefix + str(i), "entity_ids": entity_ids}
            )
    return unified_example