upload hubscripts/cellfinder_hub.py to hub from bigbio repo

cellfinder.py

@@ -0,0 +1,277 @@
+# 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.
+"""
+The CellFinder project aims to create a stem cell data repository by linking
+information from existing public databases and by performing text mining on the
+research literature. The first version of the corpus is composed of 10 full text
+documents containing more than 2,100 sentences, 65,000 tokens and 5,200
+annotations for entities. The corpus has been annotated with six types of
+entities (anatomical parts, cell components, cell lines, cell types,
+genes/protein and species) with an overall inter-annotator agreement around 80%.
+
+See: https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/
+"""
+from pathlib import Path
+from typing import Dict, Iterator, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{neves2012annotating,
+  title        = {Annotating and evaluating text for stem cell research},
+  author       = {Neves, Mariana and Damaschun, Alexander and Kurtz, Andreas and Leser, Ulf},
+  year         = 2012,
+  booktitle    = {
+    Proceedings of the Third Workshop on Building and Evaluation Resources for
+    Biomedical Text Mining\ (BioTxtM 2012) at Language Resources and Evaluation
+    (LREC). Istanbul, Turkey
+  },
+  pages        = {16--23},
+  organization = {Citeseer}
+}
+"""
+
+_DATASETNAME = "cellfinder"
+_DISPLAYNAME = "CellFinder"
+
+_DESCRIPTION = """\
+The CellFinder project aims to create a stem cell data repository by linking \
+information from existing public databases and by performing text mining on the \
+research literature. The first version of the corpus is composed of 10 full text \
+documents containing more than 2,100 sentences, 65,000 tokens and 5,200 \
+annotations for entities. The corpus has been annotated with six types of \
+entities (anatomical parts, cell components, cell lines, cell types, \
+genes/protein and species) with an overall inter-annotator agreement around 80%.
+
+See: https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/
+"""
+
+_HOMEPAGE = (
+    "https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/"
+)
+_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported'
+
+_SOURCE_URL = (
+    "https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/"
+)
+_URLS = {
+    _DATASETNAME: _SOURCE_URL + "cellfinder1_brat.tar.gz",
+    _DATASETNAME + "_splits": _SOURCE_URL + "cellfinder1_brat_sections.tar.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class CellFinderDataset(datasets.GeneratorBasedBuilder):
+    """The CellFinder corpus."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="cellfinder_source",
+            version=SOURCE_VERSION,
+            description="CellFinder source schema",
+            schema="source",
+            subset_id="cellfinder",
+        ),
+        BigBioConfig(
+            name="cellfinder_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="CellFinder BigBio schema",
+            schema="bigbio_kb",
+            subset_id="cellfinder",
+        ),
+        BigBioConfig(
+            name="cellfinder_splits_source",
+            version=SOURCE_VERSION,
+            description="CellFinder source schema",
+            schema="source",
+            subset_id="cellfinder_splits",
+        ),
+        BigBioConfig(
+            name="cellfinder_splits_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="CellFinder BigBio schema",
+            schema="bigbio_kb",
+            subset_id="cellfinder_splits",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "cellfinder_source"
+    SPLIT_TO_IDS = {
+        "train": [16316465, 17381551, 17389645, 18162134, 18286199],
+        "test": [15971941, 16623949, 16672070, 17288595, 17967047],
+    }
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "entity_id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(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):
+        urls = _URLS[_DATASETNAME]
+        if self.config.subset_id.endswith("_splits"):
+            urls = _URLS[_DATASETNAME + "_splits"]
+
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_dir": data_dir, "split": "test"},
+            ),
+        ]
+
+    def _is_to_exclude(self, file: Path) -> bool:
+
+        to_exclude = False
+
+        if (
+            file.name.startswith("._")
+            or file.name.endswith(".ann")
+            or file.name == "LICENSE"
+        ):
+            to_exclude = True
+
+        return to_exclude
+
+    def _not_in_split(self, file: Path, split: str) -> bool:
+
+        to_exclude = False
+
+        # SKIP files according to split
+        if self.config.subset_id.endswith("_splits"):
+            file_id = file.stem.split("_")[0]
+        else:
+            file_id = file.stem
+
+        if int(file_id) not in self.SPLIT_TO_IDS[split]:
+            to_exclude = True
+
+        return to_exclude
+
+    def _generate_examples(
+        self, data_dir: Path, split: str
+    ) -> Iterator[Tuple[str, Dict]]:
+        if self.config.schema == "source":
+            for file in data_dir.iterdir():
+
+                # Ignore hidden files and annotation files - we just consider the brat text files
+                if self._is_to_exclude(file=file):
+                    continue
+
+                if self._not_in_split(file=file, split=split):
+                    continue
+
+                # Read brat annotations for the given text file and convert example to the source format
+                brat_example = parsing.parse_brat_file(file)
+                source_example = self._to_source_example(file, brat_example)
+
+                yield source_example["document_id"], source_example
+
+        elif self.config.schema == "bigbio_kb":
+            for file in data_dir.iterdir():
+
+                # Ignore hidden files and annotation files - we just consider the brat text files
+                if self._is_to_exclude(file=file):
+                    continue
+
+                if self._not_in_split(file=file, split=split):
+                    continue
+
+                # Read brat annotations for the given text file and convert example to the BigBio-KB format
+                brat_example = parsing.parse_brat_file(file)
+                kb_example = parsing.brat_parse_to_bigbio_kb(brat_example)
+                kb_example["id"] = kb_example["document_id"]
+
+                # Fix text type annotation for the converted example
+                kb_example["passages"][0]["type"] = self.get_text_type(file)
+
+                yield kb_example["id"], kb_example
+
+    def _to_source_example(self, input_file: Path, brat_example: Dict) -> Dict:
+        """
+        Converts an example extracted using the default brat parsing logic to the source format
+        of the given corpus.
+        """
+        text_type = self.get_text_type(input_file)
+        source_example = {
+            "document_id": brat_example["document_id"],
+            "text": brat_example["text"],
+            "type": text_type,
+        }
+
+        id_prefix = brat_example["document_id"] + "_"
+
+        source_example["entities"] = []
+        for entity_annotation in brat_example["text_bound_annotations"]:
+            entity_ann = entity_annotation.copy()
+
+            entity_ann["entity_id"] = id_prefix + entity_ann["id"]
+            entity_ann.pop("id")
+
+            source_example["entities"].append(entity_ann)
+
+        return source_example
+
+    def get_text_type(self, input_file: Path) -> str:
+        """
+        Exctracts section name from filename, if absent return full_text
+        """
+
+        name_parts = str(input_file.stem).split("_")
+        if len(name_parts) == 3:
+            return name_parts[2]
+        return "full_text"