upload hubscripts/nlmchem_hub.py to hub from bigbio repo

nlmchem.py

@@ -0,0 +1,371 @@
+# 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{islamaj2021nlm,
+title={NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature},
+author={Islamaj, Rezarta and Leaman, Robert and Kim, Sun and Kwon, Dongseop and Wei, Chih-Hsuan and Comeau, Donald C and Peng, Yifan and Cissel, David and Coss, Cathleen and Fisher, Carol and others},
+journal={Scientific Data},
+volume={8},
+number={1},
+pages={1--12},
+year={2021},
+publisher={Nature Publishing Group}
+}
+"""
+
+_DATASETNAME = "nlmchem"
+_DISPLAYNAME = "NLM-Chem"
+
+_DESCRIPTION = """\
+NLM-Chem corpus consists of 150 full-text articles from the PubMed Central Open Access dataset,
+comprising 67 different chemical journals, aiming to cover a general distribution of usage of chemical
+names in the biomedical literature.
+Articles were selected so that human annotation was most valuable (meaning that they were rich in bio-entities,
+and current state-of-the-art named entity recognition systems disagreed on bio-entity recognition.
+"""
+
+_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-2"
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+# files found here `https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/` have issues at extraction
+# _URLs = {"biocreative": "https://ftp.ncbi.nlm.nih.gov/pub/lu/NLMChem" }
+_URLs = {
+    "source": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-NLMChem-corpus_v2.BioC.xml.gz",
+    "bigbio_kb": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-NLMChem-corpus_v2.BioC.xml.gz",
+    "bigbio_text": "https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/BC7T2-NLMChem-corpus_v2.BioC.xml.gz",
+}
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.NAMED_ENTITY_DISAMBIGUATION,
+    Tasks.TEXT_CLASSIFICATION,
+]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class NLMChemDataset(datasets.GeneratorBasedBuilder):
+    """NLMChem"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="nlmchem_source",
+            version=SOURCE_VERSION,
+            description="NLM_Chem source schema",
+            schema="source",
+            subset_id="nlmchem",
+        ),
+        BigBioConfig(
+            name="nlmchem_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="NLM_Chem BigBio schema (KB)",
+            schema="bigbio_kb",
+            subset_id="nlmchem",
+        ),
+        BigBioConfig(
+            name="nlmchem_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="NLM_Chem BigBio schema (TEXT)",
+            schema="bigbio_text",
+            subset_id="nlmchem",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "nlmchem_source"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    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(
+            # 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=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=str(_LICENSE),
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        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-NLMChem-corpus-train.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-NLMChem-corpus-test.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-NLMChem-corpus-dev.BioC.xml"
+                    ),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    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 _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 _, 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)
+
+            # annotation used only for document indexing
+            if p.text is None:
+                continue
+
+            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")
+
+                # no in-text annotation: only for document indexing
+                if (
+                    self.config.schema == "bigbio_kb"
+                    and a_type == "MeSH_Indexing_Chemical"
+                ):
+                    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:
+
+            normalized = [{"db_name": "-1", "db_id": "-1"}]
+
+        return normalized
+
+    def _generate_examples(
+        self,
+        filepath: str,
+        split: str,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ) -> 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_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
+
+        elif self.config.schema == "bigbio_kb":
+            uid = 0
+            for idx, doc in enumerate(reader):
+
+                # global id
+                uid += 1
+
+                passages, passages_entities = self._get_passages_and_entities(doc)
+
+                # 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
+
+                # if split == "validation" and uid == 6705:
+                #     breakpoint()
+
+                yield idx, {
+                    "id": uid,
+                    "document_id": doc.id,
+                    "passages": passages,
+                    "entities": entities,
+                    "events": [],
+                    "coreferences": [],
+                    "relations": [],
+                }