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"""RE Dataset, Arkhn style."""
import itertools
import os
import zipfile
from dataclasses import dataclass
from glob import glob
from pathlib import Path
from typing import Optional
import datasets
from cassis import Cas, load_cas_from_xmi, load_typesystem
# You can copy an official description
_DESCRIPTION = (
"This dataset is designed to solve the great task of Relation Extraction and "
"is crafted with a lot of care."
)
SENTENCE_CAS = "de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence"
CUSTOM_RELATION_CAS = "custom.Relation"
DOCUMENT_METADATA = "de.tudarmstadt.ukp.dkpro.core.api.metadata.type.DocumentMetaData"
CUSTOM_SPAN = "custom.Span"
@dataclass
class ReMedicalAnnotationsConfig(datasets.BuilderConfig):
"""BuilderConfig for ReMedicalAnnotations dataset."""
labels: Optional[list[str]] = None
class ReMedicalAnnotations(datasets.GeneratorBasedBuilder):
"""This dataset is designed to solve the great task of Relation Extraction and is crafted
with a lot of care."""
BUILDER_CONFIG_CLASS = ReMedicalAnnotationsConfig
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"text": datasets.Value("string"),
"subj_start": datasets.Value("int32"),
"subj_end": datasets.Value("int32"),
"subj_type": datasets.Value("string"),
"obj_start": datasets.Value("int32"),
"obj_end": datasets.Value("int32"),
"obj_type": datasets.Value("string"),
"relation": datasets.ClassLabel(names=["no_relation"] + self.config.labels),
}
),
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.extract(self.config.data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "all",
},
)
]
@staticmethod
def get_cas_objects(filepath: str):
cas_objects: list[Cas] = []
curation_path = os.path.join(filepath, "curation")
for zip_subset_path in sorted(glob(curation_path + "/**/*.zip")):
with zipfile.ZipFile(zip_subset_path) as zip_subset:
subset_folder = str(Path(zip_subset_path).parent)
zip_subset.extractall(subset_folder)
with open(glob(subset_folder + "/*.xml")[0], "rb") as f:
typesystem = load_typesystem(f)
with open(glob(subset_folder + "/*.xmi")[0], "rb") as f:
cas = load_cas_from_xmi(f, typesystem=typesystem)
cas_objects.append(cas)
return cas_objects
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath: str, split: str):
"""Generate RE examples from an unzipped Inception dataset."""
key = 0
for cas in self.get_cas_objects(filepath=filepath):
examples = cas.select(SENTENCE_CAS)
for example in examples:
offset = (
cas.select(DOCUMENT_METADATA)[0]
.get_covered_text()
.find(example.get_covered_text())
)
# retrieve all the relations as tuples (dependant, governor) with the span ids
# and the corresponding relation label
relations = {}
for relation in cas.select_covered(CUSTOM_RELATION_CAS, example):
relations[(relation.Dependent.xmiID, relation.Governor.xmiID)] = relation.label
# Create all possible combinations of 2 entities (we keep in undirected for now)
entities = cas.select_covered(CUSTOM_SPAN, example)
combinations = itertools.combinations(entities, 2)
for ent1, ent2 in combinations:
if (ent1.xmiID, ent2.xmiID) in relations.keys():
relation = relations[(ent1.xmiID, ent2.xmiID)]
else:
relation = "no_relation"
yield key, {
"text": example.get_covered_text(),
"subj_start": ent1.begin - offset,
"subj_end": ent1.end - offset,
"subj_type": ent1.label,
"obj_start": ent2.begin - offset,
"obj_end": ent2.end - offset,
"obj_type": ent2.label,
"relation": relation,
}
key += 1
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