Create MANTRAGSC.py

MANTRAGSC.py

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+# 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.
+
+# pip install xmltodict
+
+import random
+from pathlib import Path
+from itertools import product
+from dataclasses import dataclass
+from typing import Dict, List, Tuple
+
+import xmltodict
+import numpy as np
+
+import datasets
+
+_CITATION = """\
+@article{10.1093/jamia/ocv037,
+	author = {Kors, Jan A and Clematide, Simon and Akhondi,
+	Saber A and van Mulligen, Erik M and Rebholz-Schuhmann, Dietrich},
+	title = "{A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC}",
+	journal = {Journal of the American Medical Informatics Association},
+	volume = {22},
+	number = {5},
+	pages = {948-956},
+	year = {2015},
+	month = {05},
+	abstract = "{Objective To create a multilingual gold-standard corpus for biomedical concept recognition.Materials
+	and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels,
+	biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language
+	independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and
+	covering a wide range of semantic groups. To reduce the annotation workload, automatically generated
+	preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and
+	cross-language consistency checks were carried out to arrive at the final annotations.Results The number of final
+	annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are
+	similar to those between individual annotators and the gold standard. The automatically generated harmonized
+	annotation set for each language performed equally well as the best annotator for that language.Discussion The use
+	of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation
+	efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance
+	of automatic annotation techniques.Conclusion To our knowledge, this is the first gold-standard corpus for
+	biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety
+	of semantic groups that are being covered, and the diversity of text genres that were annotated.}",
+	issn = {1067-5027},
+	doi = {10.1093/jamia/ocv037},
+	url = {https://doi.org/10.1093/jamia/ocv037},
+	eprint = {https://academic.oup.com/jamia/article-pdf/22/5/948/34146393/ocv037.pdf},
+}
+"""
+
+_DESCRIPTION = """\
+We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims)
+in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical
+concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups.
+"""
+
+_HOMEPAGE = "https://biosemantics.erasmusmc.nl/index.php/resources/mantra-gsc"
+
+_LICENSE = "CC_BY_4p0"
+
+_URL = "https://files.ifi.uzh.ch/cl/mantra/gsc/GSC-v1.1.zip"
+
+_LANGUAGES_2 = {
+	"es": "Spanish",
+	"fr": "French",
+	"de": "German",
+	"nl": "Dutch",
+	"en": "English",
+}
+
+_DATASET_TYPES = {
+	"emea": "EMEA",
+	"medline": "Medline",
+	"patents": "Patent",
+}
+
+@dataclass
+class DrBenchmarkConfig(datasets.BuilderConfig):
+	name: str = None
+	version: datasets.Version = None
+	description: str = None
+	schema: str = None
+	subset_id: str = None
+
+class MANTRAGSC(datasets.GeneratorBasedBuilder):
+
+	SOURCE_VERSION = datasets.Version("1.0.0")
+
+	BUILDER_CONFIGS = []
+
+	for language, dataset_type in product(_LANGUAGES_2, _DATASET_TYPES):
+
+		if dataset_type == "patents" and language in ["nl", "es"]:
+			continue
+
+		BUILDER_CONFIGS.append(
+			DrBenchmarkConfig(
+				name=f"{language}_{dataset_type}",
+				version=SOURCE_VERSION,
+				description=f"Mantra GSC {_LANGUAGES_2[language]} {_DATASET_TYPES[dataset_type]} source schema",
+				schema="source",
+				subset_id=f"{language}_{_DATASET_TYPES[dataset_type]}",
+			)
+		)
+
+	DEFAULT_CONFIG_NAME = "fr_medline"
+
+	def _info(self):
+
+		if self.config.name.find("emea") != -1:
+			names = ['B-ANAT', 'I-ANAT', 'I-PHEN', 'B-PROC', 'I-CHEM', 'I-PHYS', 'B-DEVI', 'O', 'B-PHYS', 'I-DEVI', 'B-OBJC', 'I-DISO', 'B-PHEN', 'I-LIVB', 'B-DISO', 'B-LIVB', 'B-CHEM', 'I-PROC']
+		elif self.config.name.find("medline") != -1:
+			names = ['B-ANAT', 'I-ANAT', 'B-PROC', 'I-CHEM', 'I-PHYS', 'B-GEOG', 'B-DEVI', 'O', 'B-PHYS', 'I-LIVB', 'B-OBJC', 'I-DISO', 'I-DEVI', 'B-PHEN', 'B-DISO', 'B-LIVB', 'B-CHEM', 'I-PROC']
+		elif self.config.name.find("patents") != -1:
+			names = ['B-ANAT', 'I-ANAT', 'B-PROC', 'I-CHEM', 'I-PHYS', 'B-DEVI', 'O', 'I-LIVB', 'B-OBJC', 'I-DISO', 'B-PHEN', 'I-PROC', 'B-DISO', 'I-DEVI', 'B-LIVB', 'B-CHEM', 'B-PHYS']
+		
+		features = datasets.Features(
+			{
+				"id": datasets.Value("string"),
+				"tokens": [datasets.Value("string")],
+				"ner_tags": datasets.Sequence(
+					datasets.features.ClassLabel(
+						names = names,
+					)
+				),
+			}
+		)
+
+		return datasets.DatasetInfo(
+			description=_DESCRIPTION,
+			features=features,
+			homepage=_HOMEPAGE,
+			license=str(_LICENSE),
+			citation=_CITATION,
+		)
+
+	def _split_generators(self, dl_manager):
+
+		language, dataset_type = self.config.name.split("_")
+
+		data_dir = dl_manager.download_and_extract(_URL)
+		data_dir = Path(data_dir) / "GSC-v1.1" / f"{_DATASET_TYPES[dataset_type]}_GSC_{language}_man.xml"
+
+		return [
+			datasets.SplitGenerator(
+				name=datasets.Split.TRAIN,
+				gen_kwargs={
+					"data_dir": data_dir,
+					"split": "train",
+				},
+			),
+			datasets.SplitGenerator(
+				name=datasets.Split.VALIDATION,
+				gen_kwargs={
+					"data_dir": data_dir,
+					"split": "validation",
+				},
+			),
+			datasets.SplitGenerator(
+				name=datasets.Split.TEST,
+				gen_kwargs={
+					"data_dir": data_dir,
+					"split": "test",
+				},
+			),
+		]
+
+	def _generate_examples(self, data_dir, split):
+
+		with open(data_dir) as fd:
+			doc = xmltodict.parse(fd.read())
+
+		all_res = []
+
+		for d in doc["Corpus"]["document"]:
+
+			if type(d["unit"]) != type(list()):
+				d["unit"] = [d["unit"]]
+
+			for u in d["unit"]:
+
+				text = u["text"]
+
+				if "e" in u.keys():
+
+					if type(u["e"]) != type(list()):
+						u["e"] = [u["e"]]
+					
+					tags = [{
+						"label": current["@grp"].upper(),
+						"offset_start": int(current["@offset"]),
+						"offset_end": int(current["@offset"]) + int(current["@len"]),
+					} for current in u["e"]]
+
+				else:
+					tags = []
+
+				_tokens = text.split(" ")
+				tokens = []
+				for i, t in enumerate(_tokens):
+
+					concat = " ".join(_tokens[0:i+1])
+
+					offset_start = len(concat) - len(t)
+					offset_end = len(concat)
+
+					tokens.append({
+						"token": t,
+						"offset_start": offset_start,
+						"offset_end": offset_end,
+					})
+
+				ner_tags = [["O", 0] for o in tokens]
+
+				for tag in tags:
+
+					cpt = 0
+
+					for idx, token in enumerate(tokens):
+
+						rtok = range(token["offset_start"], token["offset_end"]+1)
+						rtag = range(tag["offset_start"], tag["offset_end"]+1)
+
+						# Check if the ranges are overlapping
+						if bool(set(rtok) & set(rtag)):
+
+							# if ner_tags[idx] != "O" and ner_tags[idx] != tag['label']:
+							# 	print(f"{token} - currently: {ner_tags[idx]} - after: {tag['label']}")
+							
+							if ner_tags[idx][0] == "O":
+								cpt += 1
+								ner_tags[idx][0] = tag["label"]
+								ner_tags[idx][1] = cpt
+
+				for i in range(len(ner_tags)):
+
+					tag = ner_tags[i][0]
+
+					if tag == "O":
+						continue
+					elif tag != "O" and ner_tags[i][1] == 1:
+						ner_tags[i][0] = "B-" + tag
+					elif tag != "O" and ner_tags[i][1] != 1:
+						ner_tags[i][0] = "I-" + tag
+
+				obj = {
+					"id": u["@id"],
+					"tokens": [t["token"] for t in tokens],
+					"ner_tags": [n[0] for n in ner_tags],
+				}
+
+				all_res.append(obj)
+		
+		ids = [r["id"] for r in all_res]
+
+		random.seed(4)
+		random.shuffle(ids)
+		random.shuffle(ids)
+		random.shuffle(ids)
+
+		train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
+
+		if split == "train":
+			allowed_ids = list(train)
+		elif split == "validation":
+			allowed_ids = list(validation)
+		elif split == "test":
+			allowed_ids = list(test)
+		
+		for r in all_res:
+			identifier = r["id"]
+			if identifier in allowed_ids:
+				yield identifier, r