upload hubscripts/tmvar_v1_hub.py to hub from bigbio repo

tmvar_v1.py

@@ -0,0 +1,249 @@
+# 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 itertools
+import os
+from pydoc import doc
+from typing import Dict, Iterator, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{wei2013tmvar,
+  title={tmVar: a text mining approach for extracting sequence variants in biomedical literature},
+  author={Wei, Chih-Hsuan and Harris, Bethany R and Kao, Hung-Yu and Lu, Zhiyong},
+  journal={Bioinformatics},
+  volume={29},
+  number={11},
+  pages={1433--1439},
+  year={2013},
+  publisher={Oxford University Press}
+}
+"""
+
+_DATASETNAME = "tmvar_v1"
+_DISPLAYNAME = "tmVar v1"
+
+_DESCRIPTION = """This dataset contains 500 PubMed articles manually annotated with mutation mentions of various kinds. It can be used for NER tasks only.
+The dataset is split into train(334) and test(166) splits"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/tmVar/tmVarCorpus.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class TmvarV1Dataset(datasets.GeneratorBasedBuilder):
+    """
+    The tmVar dataset contains 500 PubMed articles manually annotated with mutation
+    mentions of various kinds.
+    It can be used for biomedical NER tasks
+    """
+
+    DEFAULT_CONFIG_NAME = "tmvar_v1_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+    BUILDER_CONFIGS.append(
+        BigBioConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        )
+    )
+    BUILDER_CONFIGS.append(
+        BigBioConfig(
+            name=f"{_DATASETNAME}_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description=f"{_DATASETNAME} BigBio schema",
+            schema="bigbio_kb",
+            subset_id=f"{_DATASETNAME}",
+        )
+    )
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "passages": [
+                        {
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offsets": [datasets.Value("int32")],
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "text": datasets.Value("string"),
+                            "offsets": [datasets.Value("int32")],
+                            "concept_id": datasets.Value("string"),
+                            "semantic_type_id": 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."""
+
+        url = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(url)
+        train_filepath = os.path.join(data_dir, "tmVarCorpus", "train.PubTator.txt")
+        test_filepath = os.path.join(data_dir, "tmVarCorpus", "test.PubTator.txt")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_filepath,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_filepath,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath) -> 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):
+                    document = self.parse_raw_doc(raw_document)
+                    pmid = document.pop("pmid")
+                    document["id"] = next(uid)
+                    document["document_id"] = pmid
+
+                    entities_ = []
+                    for entity in document["entities"]:
+                        entities_.append(
+                            {
+                                "id": next(uid),
+                                "type": entity["semantic_type_id"],
+                                "text": [entity["text"]],
+                                "normalized": [],
+                                "offsets": [entity["offsets"]],
+                            }
+                        )
+                    for passage in document["passages"]:
+                        passage["id"] = next(uid)
+
+                    document["entities"] = entities_
+                    document["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("|")
+
+        if self.config.schema == "source":
+            passages = [
+                {"type": "title", "text": title, "offsets": [0, len(title)]},
+                {
+                    "type": "abstract",
+                    "text": abstract,
+                    "offsets": [len(title) + 1, len(title) + len(abstract) + 1],
+                },
+            ]
+        elif self.config.schema == "bigbio_kb":
+            passages = [
+                {"type": "title", "text": [title], "offsets": [[0, len(title)]]},
+                {
+                    "type": "abstract",
+                    "text": [abstract],
+                    "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]],
+                },
+            ]
+
+        entities = []
+        for line in raw_doc[2:]:
+            mentions = line.split("\t")
+            (
+                pmid_,
+                start_idx,
+                end_idx,
+                mention,
+                semantic_type_id,
+                entity_id,
+            ) = mentions
+
+            entity = {
+                "offsets": [int(start_idx), int(end_idx)],
+                "text": mention,
+                "semantic_type_id": semantic_type_id,
+                "concept_id": entity_id,
+            }
+            entities.append(entity)
+
+        return {"pmid": pmid, "passages": passages, "entities": entities}