upload hubscripts/biored_hub.py to hub from bigbio repo

biored.py

@@ -0,0 +1,323 @@
+# 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.
+
+"""
+Relation Extraction corpus with multiple entity types (e.g., gene/protein,
+disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
+on a set of 600 PubMed articles
+"""
+
+import itertools
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+from bioc import pubtator
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2204-04263,
+  author    = {Ling Luo and
+               Po{-}Ting Lai and
+               Chih{-}Hsuan Wei and
+               Cecilia N. Arighi and
+               Zhiyong Lu},
+  title     = {BioRED: {A} Comprehensive Biomedical Relation Extraction Dataset},
+  journal   = {CoRR},
+  volume    = {abs/2204.04263},
+  year      = {2022},
+  url       = {https://doi.org/10.48550/arXiv.2204.04263},
+  doi       = {10.48550/arXiv.2204.04263},
+  eprinttype = {arXiv},
+  eprint    = {2204.04263},
+  timestamp = {Wed, 11 May 2022 15:24:37 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2204-04263.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DATASETNAME = "biored"
+_DISPLAYNAME = "BioRED"
+
+_DESCRIPTION = """\
+Relation Extraction corpus with multiple entity types (e.g., gene/protein,
+disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
+on a set of 600 PubMed articles
+"""
+
+_HOMEPAGE = "https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/BIORED.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class BioredDataset(datasets.GeneratorBasedBuilder):
+    """Relation Extraction corpus with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles"""
+
+    # For bigbio_kb, this dataset uses a naming convention as
+    # uid_[title/abstract/relation/entity_id]_[entity/relation_uid]
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name=_DATASETNAME + "_source",
+            version=SOURCE_VERSION,
+            description=_DATASETNAME + " source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        BigBioConfig(
+            name=_DATASETNAME + "_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description=_DATASETNAME + " BigBio schema",
+            schema="bigbio_kb",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = _DATASETNAME + "_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "passages": [
+                        {
+                            "type": datasets.Value("string"),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "concept_id": datasets.Value("string"),
+                            "semantic_type_id": datasets.Sequence(
+                                datasets.Value("string")
+                            ),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "novel": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "concept_1": datasets.Value("string"),
+                            "concept_2": datasets.Value("string"),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "BioRED", "Train.PubTator"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "BioRED", "Test.PubTator"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "BioRED", "Dev.PubTator"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath, "r", encoding="utf8") as fstream:
+                for raw_document in self.generate_raw_docs(fstream):
+                    document = self.parse_raw_doc(raw_document)
+                    yield document["pmid"], document
+
+        elif self.config.schema == "bigbio_kb":
+            with open(filepath, "r", encoding="utf8") as fstream:
+                uid = itertools.count(0)
+                for raw_document in self.generate_raw_docs(fstream):
+                    entities_in_doc = dict()
+                    document = self.parse_raw_doc(raw_document)
+                    pmid = document.pop("pmid")
+                    document["id"] = str(next(uid))
+                    document["document_id"] = pmid
+                    entities_ = []
+                    relations_ = []
+                    for entity in document["entities"]:
+                        temp_id = document["id"] + "_" + str(entity["concept_id"])
+                        curr_entity_count = entities_in_doc.get(temp_id, 0)
+                        entities_.append(
+                            {
+                                "id": temp_id + "_" + str(curr_entity_count),
+                                "type": entity["semantic_type_id"],
+                                "text": entity["text"],
+                                "normalized": [],
+                                "offsets": entity["offsets"],
+                            }
+                        )
+                        entities_in_doc[temp_id] = curr_entity_count + 1
+                    rel_uid = itertools.count(0)
+                    for relation in document["relations"]:
+                        relations_.append(
+                            {
+                                "id": document["id"]
+                                + "_relation_"
+                                + str(next(rel_uid)),
+                                "type": relation["type"],
+                                "arg1_id": document["id"]
+                                + "_"
+                                + str(relation["concept_1"])
+                                + "_0",
+                                "arg2_id": document["id"]
+                                + "_"
+                                + str(relation["concept_2"])
+                                + "_0",
+                                "normalized": [],
+                            }
+                        )
+                    for passage in document["passages"]:
+                        passage["id"] = document["id"] + "_" + passage["type"]
+                    document["entities"] = entities_
+                    document["relations"] = relations_
+                    document["events"] = []
+                    document["coreferences"] = []
+                    yield document["document_id"], document
+
+    def generate_raw_docs(self, fstream):
+        """
+        Given a filestream, this function yields documents from it
+        """
+        raw_document = []
+        for line in fstream:
+            if line.strip():
+                raw_document.append(line.strip())
+            elif raw_document:
+                yield raw_document
+                raw_document = []
+        if raw_document:
+            yield raw_document
+
+    def parse_raw_doc(self, raw_doc):
+        pmid, _, title = raw_doc[0].split("|")
+        pmid = int(pmid)
+        _, _, abstract = raw_doc[1].split("|")
+        passages = [
+            {"type": "title", "text": [title], "offsets": [[0, len(title)]]},
+            {
+                "type": "abstract",
+                "text": [abstract],
+                "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]],
+            },
+        ]
+        entities = []
+        relations = []
+        for line in raw_doc[2:]:
+            mentions = line.split("\t")
+            (_pmid, _type_ind, *rest) = mentions
+            if _type_ind in [
+                "Positive_Correlation",
+                "Association",
+                "Negative_Correlation",
+                "Bind",
+                "Conversion",
+                "Cotreatment",
+                "Cause",
+                "Comparison",
+                "Drug_Interaction",
+            ]:
+                # Relations handled here
+                relation_type = _type_ind
+                concept_1, concept_2, novel = rest
+                relation = {
+                    "type": relation_type,
+                    "concept_1": concept_1,
+                    "concept_2": concept_2,
+                    "novel": novel,
+                }
+                relations.append(relation)
+            elif _type_ind.isnumeric():
+                # Entities handled here
+                start_idx = _type_ind
+                end_idx, mention, semantic_type_id, entity_ids = rest
+                entity = [
+                    {
+                        "offsets": [[int(start_idx), int(end_idx)]],
+                        "text": [mention],
+                        "semantic_type_id": semantic_type_id.split(","),
+                        "concept_id": entity_id,
+                    }
+                    for entity_id in entity_ids.split(",")
+                ]
+                entities.extend(entity)
+            else:
+                logger.warn(
+                    f"Skipping annotation in Document ID: {_pmid}. Unexpected format"
+                )
+        return {
+            "pmid": pmid,
+            "passages": passages,
+            "entities": entities,
+            "relations": relations,
+        }