upload hubscripts/gnormplus_hub.py to hub from bigbio repo

gnormplus.py

@@ -0,0 +1,260 @@
+# 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
+import re
+from typing import Dict, List, Tuple
+
+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{Wei2015,
+author={Wei, Chih-Hsuan and Kao, Hung-Yu and Lu, Zhiyong},
+title={GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains},
+journal={BioMed Research International},
+year={2015},
+month={Aug},
+day={25},
+publisher={Hindawi Publishing Corporation},
+volume={2015},
+pages={918710},
+issn={2314-6133},
+doi={10.1155/2015/918710},
+url={https://doi.org/10.1155/2015/918710}
+}
+"""
+
+_DATASETNAME = "gnormplus"
+_DISPLAYNAME = "GNormPlus"
+
+_DESCRIPTION = """\
+We re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN
+tools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).
+The Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts
+with both mention-level and document-level annotations. They are selected because both have a focus on human genes.
+For both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also
+added mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles.
+PubTator was used as our annotation tool along with BioC formats.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/GNormPlus/GNormPlusCorpus.zip"
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class GnormplusDataset(datasets.GeneratorBasedBuilder):
+    """Dataset loader for GNormPlus corpus."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="gnormplus_source",
+            version=SOURCE_VERSION,
+            description="gnormplus source schema",
+            schema="source",
+            subset_id="gnormplus",
+        ),
+        BigBioConfig(
+            name="gnormplus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="gnormplus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="gnormplus",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "gnormplus_source"
+
+    _re_tax_id = re.compile(r"(?P<db_id>\d+)\(Tax:(?P<tax_id>\d+)\)")
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "doc_id": datasets.Value("string"),
+                    "passages": [
+                        {
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "location": {
+                                "offset": datasets.Value("int64"),
+                                "length": datasets.Value("int64"),
+                            },
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "normalized": [
+                                {
+                                    "db_name": datasets.Value("string"),
+                                    "db_id": datasets.Value("string"),
+                                    "tax_id": datasets.Value("string"),
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+        else:
+            raise NotImplementedError(self.config.schema)
+
+        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, "GNormPlusCorpus/BC2GNtrain.BioC.xml"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "GNormPlusCorpus/BC2GNtest.BioC.xml"
+                    ),
+                },
+            ),
+        ]
+
+    def _parse_bioc_entity(self, uid, bioc_ann, db_id_key="NCBI", insert_tax_id=False):
+        offsets, texts = get_texts_and_offsets_from_bioc_ann(bioc_ann)
+        _type = bioc_ann.infons["type"]
+
+        # parse db ids
+        normalized = []
+        if _type in bioc_ann.infons:
+            for _id in bioc_ann.infons[_type].split(","):
+                match = self._re_tax_id.match(_id)
+                if match:
+                    _id = match.group("db_id")
+
+                n = {"db_name": db_id_key, "db_id": _id}
+                if insert_tax_id:
+                    n["tax_id"] = match.group("tax_id") if match else None
+
+                normalized.append(n)
+        return {
+            "id": uid,
+            "offsets": offsets,
+            "text": texts,
+            "type": _type,
+            "normalized": normalized,
+        }
+
+    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
+        uid = map(str, itertools.count(start=0, step=1))
+
+        with open(filepath, "r") as fp:
+            collection = biocxml.load(fp)
+
+            for idx, document in enumerate(collection.documents):
+                if self.config.schema == "source":
+                    features = {
+                        "doc_id": document.id,
+                        "passages": [
+                            {
+                                "text": passage.text,
+                                "type": passage.infons["type"],
+                                "location": {
+                                    "offset": passage.offset,
+                                    "length": passage.total_span.length,
+                                },
+                            }
+                            for passage in document.passages
+                        ],
+                        "entities": [
+                            self._parse_bioc_entity(
+                                next(uid), entity, insert_tax_id=True
+                            )
+                            for passage in document.passages
+                            for entity in passage.annotations
+                        ],
+                    }
+                    yield idx, features
+                elif self.config.schema == "bigbio_kb":
+                    # passage offsets/lengths do not connect, recalculate them for this schema.
+                    passage_spans = []
+                    start = 0
+                    for passage in document.passages:
+                        end = start + len(passage.text)
+                        passage_spans.append((start, end))
+                        start = end + 1
+
+                    features = {
+                        "id": next(uid),
+                        "document_id": document.id,
+                        "passages": [
+                            {
+                                "id": next(uid),
+                                "type": passage.infons["type"],
+                                "text": [passage.text],
+                                "offsets": [span],
+                            }
+                            for passage, span in zip(document.passages, passage_spans)
+                        ],
+                        "entities": [
+                            self._parse_bioc_entity(next(uid), entity)
+                            for passage in document.passages
+                            for entity in passage.annotations
+                        ],
+                        "events": [],
+                        "coreferences": [],
+                        "relations": [],
+                    }
+                    yield idx, features
+                else:
+                    raise NotImplementedError(self.config.schema)