upload hubscripts/chemdner_hub.py to hub from bigbio repo

chemdner.py

@@ -0,0 +1,417 @@
+# 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
+import re
+from typing import Dict, Iterator, List, Tuple
+
+import bioc
+import datasets
+from bioc import biocxml
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{Krallinger2015,
+  title        = {The CHEMDNER corpus of chemicals and drugs and its annotation principles},
+  author       = {
+    Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez,
+    Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan
+    and Ji, Donghong and Lowe, Daniel M. and Sayle, Roger A. and
+    Batista-Navarro, Riza Theresa and Rak, Rafal and Huber, Torsten and
+    Rockt{\"a}schel, Tim and Matos, S{\'e}rgio and Campos, David and Tang,
+    Buzhou and Xu, Hua and Munkhdalai, Tsendsuren and Ryu, Keun Ho and Ramanan,
+    S. V. and Nathan, Senthil and {\v{Z}}itnik, Slavko and Bajec, Marko and
+    Weber, Lutz and Irmer, Matthias and Akhondi, Saber A. and Kors, Jan A. and
+    Xu, Shuo and An, Xin and Sikdar, Utpal Kumar and Ekbal, Asif and Yoshioka,
+    Masaharu and Dieb, Thaer M. and Choi, Miji and Verspoor, Karin and Khabsa,
+    Madian and Giles, C. Lee and Liu, Hongfang and Ravikumar, Komandur
+    Elayavilli and Lamurias, Andre and Couto, Francisco M. and Dai, Hong-Jie
+    and Tsai, Richard Tzong-Han and Ata, Caglar and Can, Tolga and Usi{\'e},
+    Anabel and Alves, Rui and Segura-Bedmar, Isabel and Mart{\'i}nez, Paloma
+    and Oyarzabal, Julen and Valencia, Alfonso
+  },
+  year         = 2015,
+  month        = {Jan},
+  day          = 19,
+  journal      = {Journal of Cheminformatics},
+  volume       = 7,
+  number       = 1,
+  pages        = {S2},
+  doi          = {10.1186/1758-2946-7-S1-S2},
+  issn         = {1758-2946},
+  url          = {https://doi.org/10.1186/1758-2946-7-S1-S2},
+  abstract     = {
+    The automatic extraction of chemical information from text requires the
+    recognition of chemical entity mentions as one of its key steps. When
+    developing supervised named entity recognition (NER) systems, the
+    availability of a large, manually annotated text corpus is desirable.
+    Furthermore, large corpora permit the robust evaluation and comparison of
+    different approaches that detect chemicals in documents. We present the
+    CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
+    total of 84,355 chemical entity mentions labeled manually by expert
+    chemistry literature curators, following annotation guidelines specifically
+    defined for this task. The abstracts of the CHEMDNER corpus were selected
+    to be representative for all major chemical disciplines. Each of the
+    chemical entity mentions was manually labeled according to its
+    structure-associated chemical entity mention (SACEM) class: abbreviation,
+    family, formula, identifier, multiple, systematic and trivial. The
+    difficulty and consistency of tagging chemicals in text was measured using
+    an agreement study between annotators, obtaining a percentage agreement of
+    91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts)
+    we provide not only the Gold Standard manual annotations, but also mentions
+    automatically detected by the 26 teams that participated in the BioCreative
+    IV CHEMDNER chemical mention recognition task. In addition, we release the
+    CHEMDNER silver standard corpus of automatically extracted mentions from
+    17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus
+    in the BioC format has been generated as well. We propose a standard for
+    required minimum information about entity annotations for the construction
+    of domain specific corpora on chemical and drug entities. The CHEMDNER
+    corpus and annotation guidelines are available at:
+    ttp://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
+  }
+}
+"""
+
+_DESCRIPTION = """\
+We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that
+contain a total of 84,355 chemical entity mentions labeled manually by expert
+chemistry literature curators, following annotation guidelines specifically
+defined for this task. The abstracts of the CHEMDNER corpus were selected to be
+representative for all major chemical disciplines. Each of the chemical entity
+mentions was manually labeled according to its structure-associated chemical
+entity mention (SACEM) class: abbreviation, family, formula, identifier,
+multiple, systematic and trivial.
+"""
+
+_DATASETNAME = "chemdner"
+_DISPLAYNAME = "CHEMDNER"
+
+_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/"
+
+_LICENSE = 'License information unavailable'
+
+_URLs = {
+    "source": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-CHEMDNER-corpus_v2.BioC.xml.gz",
+    "bigbio_kb": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-CHEMDNER-corpus_v2.BioC.xml.gz",
+    "bigbio_text": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-CHEMDNER-corpus_v2.BioC.xml.gz",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.TEXT_CLASSIFICATION,
+]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class CHEMDNERDataset(datasets.GeneratorBasedBuilder):
+    """CHEMDNER"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="chemdner_source",
+            version=SOURCE_VERSION,
+            description="CHEMDNER source schema",
+            schema="source",
+            subset_id="chemdner",
+        ),
+        BigBioConfig(
+            name="chemdner_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="CHEMDNER BigBio schema (KB)",
+            schema="bigbio_kb",
+            subset_id="chemdner",
+        ),
+        BigBioConfig(
+            name="chemdner_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="CHEMDNER BigBio schema (TEXT)",
+            schema="bigbio_text",
+            subset_id="chemdner",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "chemdner_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            # this is a variation on the BioC format
+            features = datasets.Features(
+                {
+                    "passages": [
+                        {
+                            "document_id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offset": datasets.Value("int32"),
+                            "entities": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "offsets": [[datasets.Value("int32")]],
+                                    "text": [datasets.Value("string")],
+                                    "type": datasets.Value("string"),
+                                    "normalized": [
+                                        {
+                                            "db_name": datasets.Value("string"),
+                                            "db_id": datasets.Value("string"),
+                                        }
+                                    ],
+                                }
+                            ],
+                        }
+                    ]
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        elif self.config.schema == "bigbio_text":
+            features = text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = dl_manager.download_and_extract(my_urls) + "/"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "BC7T2-CHEMDNER-corpus-training.BioC.xml"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "BC7T2-CHEMDNER-corpus-evaluation.BioC.xml"
+                    ),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "BC7T2-CHEMDNER-corpus-development.BioC.xml"
+                    ),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _get_passages_and_entities(
+        self, d: bioc.BioCDocument
+    ) -> Tuple[List[Dict], List[List[Dict]]]:
+
+        passages: List[Dict] = []
+        entities: List[List[Dict]] = []
+
+        text_total_length = 0
+
+        po_start = 0
+
+        for i, p in enumerate(d.passages):
+
+            eo = p.offset - text_total_length
+
+            text_total_length += len(p.text) + 1
+
+            po_end = po_start + len(p.text)
+
+            dp = {
+                "text": p.text,
+                "type": p.infons.get("type"),
+                "offsets": [(po_start, po_end)],
+                "offset": p.offset,  # original offset
+            }
+
+            po_start = po_end + 1
+
+            passages.append(dp)
+
+            pe = []
+
+            for a in p.annotations:
+
+                a_type = a.infons.get("type")
+
+                if (
+                    self.config.schema == "bigbio_kb"
+                    and a_type == "MeSH_Indexing_Chemical"
+                ):
+                    continue
+
+                if (
+                    a.text == None or a.text == ""
+                ) and self.config.schema == "bigbio_kb":
+                    continue
+
+                offsets, text = get_texts_and_offsets_from_bioc_ann(a)
+
+                da = {
+                    "type": a_type,
+                    "offsets": [(start - eo, end - eo) for (start, end) in offsets],
+                    "text": text,
+                    "id": a.id,
+                    "normalized": self._get_normalized(a),
+                }
+
+                pe.append(da)
+
+            entities.append(pe)
+
+        return passages, entities
+
+    def _get_normalized(self, a: bioc.BioCAnnotation) -> List[Dict]:
+        """
+        Get normalization DB and ID from annotation identifiers
+        """
+
+        identifiers = a.infons.get("identifier")
+
+        if identifiers is not None:
+
+            identifiers = re.split(r",|;", identifiers)
+
+            identifiers = [i for i in identifiers if i != "-"]
+
+            normalized = [i.split(":") for i in identifiers]
+
+            normalized = [
+                {"db_name": elems[0], "db_id": elems[1]} for elems in normalized
+            ]
+
+        else:
+
+            # No normalization
+            normalized = []
+
+        return normalized
+
+    def _get_textcls_example(self, d: bioc.BioCDocument) -> Dict:
+
+        example = {"document_id": d.id, "text": [], "labels": []}
+
+        for p in d.passages:
+
+            example["text"].append(p.text)
+
+            for a in p.annotations:
+
+                if a.infons.get("type") == "MeSH_Indexing_Chemical":
+
+                    example["labels"].append(a.infons.get("identifier"))
+
+        example["text"] = " ".join(example["text"])
+
+        return example
+
+    def _generate_examples(
+        self,
+        filepath: str,
+        split: str,
+    ) -> Iterator[Tuple[int, Dict]]:
+        """Yields examples as (key, example) tuples."""
+
+        reader = biocxml.BioCXMLDocumentReader(str(filepath))
+
+        if self.config.schema == "source":
+
+            for uid, doc in enumerate(reader):
+
+                passages, passages_entities = self._get_passages_and_entities(doc)
+
+                for p, pe in zip(passages, passages_entities):
+
+                    p.pop("offsets")  # BioC has only start for passages offsets
+
+                    p["document_id"] = doc.id
+                    p["entities"] = pe  # BioC has per passage entities
+
+                yield uid, {"passages": passages}
+
+        elif self.config.schema == "bigbio_kb":
+
+            uid = 0
+
+            for idx, doc in enumerate(reader):
+
+                passages, passages_entities = self._get_passages_and_entities(doc)
+
+                # global id
+                uid += 1
+
+                # unpack per-passage entities
+                entities = [e for pe in passages_entities for e in pe]
+
+                for p in passages:
+                    p.pop("offset")  # drop original offset
+                    p["text"] = (p["text"],)  # text in passage is Sequence
+                    p["id"] = uid  # override BioC default id
+                    uid += 1
+
+                for e in entities:
+                    e["id"] = uid  # override BioC default id
+                    uid += 1
+
+                yield idx, {
+                    "id": uid,
+                    "document_id": doc.id,
+                    "passages": passages,
+                    "entities": entities,
+                    "events": [],
+                    "coreferences": [],
+                    "relations": [],
+                }
+
+        elif self.config.schema == "bigbio_text":
+
+            uid = 0
+
+            for idx, doc in enumerate(reader):
+
+                example = self._get_textcls_example(doc)
+                example["id"] = uid
+
+                # global id
+                uid += 1
+
+                yield idx, example