upload hubscripts/bc5cdr_hub.py to hub from bigbio repo

bc5cdr.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.
+"""
+To this end, we set up a challenge task through BioCreative V to automatically
+extract CDRs from the literature. More specifically, we designed two challenge
+tasks: disease named entity recognition (DNER) and chemical-induced disease
+(CID) relation extraction. To assist system development and assessment, we
+created a large annotated text corpus that consists of human annotations of
+all chemicals, diseases and their interactions in 1,500 PubMed articles.
+
+-- 'Overview of the BioCreative V Chemical Disease Relation (CDR) Task'
+"""
+import collections
+import itertools
+import os
+
+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{DBLP:journals/biodb/LiSJSWLDMWL16,
+  author    = {Jiao Li and
+               Yueping Sun and
+               Robin J. Johnson and
+               Daniela Sciaky and
+               Chih{-}Hsuan Wei and
+               Robert Leaman and
+               Allan Peter Davis and
+               Carolyn J. Mattingly and
+               Thomas C. Wiegers and
+               Zhiyong Lu},
+  title     = {BioCreative {V} {CDR} task corpus: a resource for chemical disease
+               relation extraction},
+  journal   = {Database J. Biol. Databases Curation},
+  volume    = {2016},
+  year      = {2016},
+  url       = {https://doi.org/10.1093/database/baw068},
+  doi       = {10.1093/database/baw068},
+  timestamp = {Thu, 13 Aug 2020 12:41:41 +0200},
+  biburl    = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DATASETNAME = "bc5cdr"
+_DISPLAYNAME = "BC5CDR"
+
+_DESCRIPTION = """\
+The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated \
+text corpus of human annotations of all chemicals, diseases and their \
+interactions in 1,500 PubMed articles.
+"""
+
+_HOMEPAGE = "http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/"
+
+_LICENSE = 'Public Domain Mark 1.0'
+
+_URLs = {
+    "source": "http://www.biocreative.org/media/store/files/2016/CDR_Data.zip",
+    "bigbio_kb": "http://www.biocreative.org/media/store/files/2016/CDR_Data.zip",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.NAMED_ENTITY_DISAMBIGUATION,
+    Tasks.RELATION_EXTRACTION,
+]
+_SOURCE_VERSION = "01.05.16"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class Bc5cdrDataset(datasets.GeneratorBasedBuilder):
+    """
+    BioCreative V Chemical Disease Relation (CDR) Task.
+    """
+
+    DEFAULT_CONFIG_NAME = "bc5cdr_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bc5cdr_source",
+            version=SOURCE_VERSION,
+            description="BC5CDR source schema",
+            schema="source",
+            subset_id="bc5cdr",
+        ),
+        BigBioConfig(
+            name="bc5cdr_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="BC5CDR simplified BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bc5cdr",
+        ),
+    ]
+
+    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"),
+                            "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"),
+                                        }
+                                    ],
+                                }
+                            ],
+                            "relations": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "type": datasets.Value("string"),
+                                    "arg1_id": datasets.Value("string"),
+                                    "arg2_id": datasets.Value("string"),
+                                }
+                            ],
+                        }
+                    ]
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_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, "CDR_Data/CDR.Corpus.v010516/CDR_TrainingSet.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, "CDR_Data/CDR.Corpus.v010516/CDR_TestSet.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,
+                        "CDR_Data/CDR.Corpus.v010516/CDR_DevelopmentSet.BioC.xml",
+                    ),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _get_bioc_entity(self, span, doc_text, db_id_key="MESH"):
+        """Parse BioC entity annotation.
+
+        Parameters
+        ----------
+        span : BioCAnnotation
+            BioC entity annotation
+        doc_text : string
+            document text, required to construct text spans
+        db_id_key : str, optional
+            database name used for normalization, by default "MESH"
+
+        Returns
+        -------
+        dict
+            entity information
+        """
+        # offsets = [(loc.offset, loc.offset + loc.length) for loc in span.locations]
+        # texts = [doc_text[i:j] for i, j in offsets]
+        offsets, texts = get_texts_and_offsets_from_bioc_ann(span)
+        db_ids = span.infons[db_id_key] if db_id_key else "-1"
+
+        # some entities are not linked and
+        # some entities are linked to multiple normalized ids
+        if db_ids == "-1":
+            db_ids_list = []
+        else:
+            db_ids_list = db_ids.split("|")
+
+        normalized = [{"db_name": db_id_key, "db_id": db_id} for db_id in db_ids_list]
+
+        return {
+            "id": span.id,
+            "offsets": offsets,
+            "text": texts,
+            "type": span.infons["type"],
+            "normalized": normalized,
+        }
+
+    def _get_relations(self, relations, entities):
+        """
+        BC5CDR provides abstract-level annotations for entity-linked relation
+        pairs rather than materializing links between all surface form
+        mentions of relations. An example from train id=2670794, the relation
+            - (chemical, disease) (D014148, D004211)
+        is materialized as 6 mentions of entity pairs
+            - 2x ('tranexamic acid', 'intravascular coagulation')
+            - 4x ('AMCA', 'intravascular coagulation')
+        """
+        # index entities by normalized id
+        index = collections.defaultdict(list)
+        for ent in entities:
+            for norm in ent["normalized"]:
+                index[norm["db_id"]].append(ent)
+        index = dict(index)
+
+        # transform doc-level relations to mention-level
+        rela_mentions = []
+        for rela in relations:
+            arg1 = rela.infons["Chemical"]
+            arg2 = rela.infons["Disease"]
+            # all mention pairs
+            all_pairs = itertools.product(index[arg1], index[arg2])
+            for a, b in all_pairs:
+                # create relations linked by entity ids
+                rela_mentions.append(
+                    {
+                        "id": None,
+                        "type": rela.infons["relation"],
+                        "arg1_id": a["id"],
+                        "arg2_id": b["id"],
+                        "normalized": [],
+                    }
+                )
+        return rela_mentions
+
+    def _get_document_text(self, xdoc):
+        """Build document text for unit testing entity span offsets."""
+        text = ""
+        for passage in xdoc.passages:
+            pad = passage.offset - len(text)
+            text += (" " * pad) + passage.text
+        return text
+
+    def _generate_examples(
+        self,
+        filepath,
+        split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """Yields examples as (key, example) tuples."""
+        if self.config.schema == "source":
+            reader = biocxml.BioCXMLDocumentReader(str(filepath))
+
+            for uid, xdoc in enumerate(reader):
+                doc_text = self._get_document_text(xdoc)
+                yield uid, {
+                    "passages": [
+                        {
+                            "document_id": xdoc.id,
+                            "type": passage.infons["type"],
+                            "text": passage.text,
+                            "entities": [
+                                self._get_bioc_entity(span, doc_text)
+                                for span in passage.annotations
+                            ],
+                            "relations": [
+                                {
+                                    "id": rel.id,
+                                    "type": rel.infons["relation"],
+                                    "arg1_id": rel.infons["Chemical"],
+                                    "arg2_id": rel.infons["Disease"],
+                                }
+                                for rel in xdoc.relations
+                            ],
+                        }
+                        for passage in xdoc.passages
+                    ]
+                }
+
+        elif self.config.schema == "bigbio_kb":
+            reader = biocxml.BioCXMLDocumentReader(str(filepath))
+            uid = 0  # global unique id
+
+            for i, xdoc in enumerate(reader):
+                data = {
+                    "id": uid,
+                    "document_id": xdoc.id,
+                    "passages": [],
+                    "entities": [],
+                    "relations": [],
+                    "events": [],
+                    "coreferences": [],
+                }
+                uid += 1
+                doc_text = self._get_document_text(xdoc)
+
+                char_start = 0
+                # passages must not overlap and spans must cover the entire document
+                for passage in xdoc.passages:
+                    offsets = [[char_start, char_start + len(passage.text)]]
+                    char_start = char_start + len(passage.text) + 1
+                    data["passages"].append(
+                        {
+                            "id": uid,
+                            "type": passage.infons["type"],
+                            "text": [passage.text],
+                            "offsets": offsets,
+                        }
+                    )
+                    uid += 1
+
+                # entities
+                for passage in xdoc.passages:
+                    for span in passage.annotations:
+                        ent = self._get_bioc_entity(span, doc_text, db_id_key="MESH")
+                        ent["id"] = uid  # override BioC default id
+                        data["entities"].append(ent)
+                        uid += 1
+
+                # relations
+                relations = self._get_relations(xdoc.relations, data["entities"])
+                for rela in relations:
+                    rela["id"] = uid  # assign unique id
+                    data["relations"].append(rela)
+                    uid += 1
+
+                yield i, data