upload hubscripts/linnaeus_hub.py to hub from bigbio repo

linnaeus.py

@@ -0,0 +1,271 @@
+# 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.
+
+"""
+LINNAEUS provides a novel corpus of full-text documents manually annotated for species mentions.
+
+To understand the true performance of the LINNAEUS system, we generated a gold standard dataset specifically
+annotated to evaluate species name identification software. The reliability of this gold standard is high,
+however some species names are likely to be omitted from this evaluation set, as shown by IAA analysis.
+Performance of species tagging by LINNAEUS on full-text articles is very good, with 94.3% recall and
+97.1% precision on mention level, and 98.1% recall and 90.4% precision on document level.
+"""
+
+import csv
+import os
+from pathlib import Path
+from typing import Dict, 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{gerner2010linnaeus,
+title={LINNAEUS: a species name identification system for biomedical literature},
+author={Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
+journal={BMC bioinformatics},
+volume={11},
+number={1},
+pages={1--17},
+year={2010},
+publisher={BioMed Central}
+}
+"""
+
+_DATASETNAME = "linnaeus"
+_DISPLAYNAME = "LINNAEUS"
+
+_DESCRIPTION = """\
+Linnaeus is a novel corpus of full-text documents manually annotated for species mentions.
+"""
+
+_HOMEPAGE = "http://linnaeus.sourceforge.net/"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "https://sourceforge.net/projects/linnaeus/files/Corpora/manual-corpus-species-1.0.tar.gz/download",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class LinnaeusDataset(datasets.GeneratorBasedBuilder):
+    """Linneaus provides a new gold-standard corpus of full-text articles
+    with manually annotated mentions of species names."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="linnaeus_source",
+            version=SOURCE_VERSION,
+            description="Linnaeus source schema",
+            schema="source",
+            subset_id="linnaeus",
+        ),
+        BigBioConfig(
+            name="linnaeus_filtered_source",
+            version=SOURCE_VERSION,
+            description="Linnaeus source schema (filtered tags)",
+            schema="source",
+            subset_id="linnaeus_filtered",
+        ),
+        BigBioConfig(
+            name="linnaeus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="Linnaeus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="linnaeus",
+        ),
+        BigBioConfig(
+            name="linnaeus_filtered_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="Linnaeus BigBio schema (filtered tags)",
+            schema="bigbio_kb",
+            subset_id="linnaeus_filtered",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "linneaus_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "document_type": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "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"),
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
+
+        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]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_files": os.path.join(data_dir, "manual-corpus-species-1.0")
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_files: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data_path = Path(os.path.join(data_files, "txt"))
+        if self.config.subset_id.endswith("filtered"):
+            tags_path = Path(os.path.join(data_files, "filtered_tags.tsv"))
+        else:
+            tags_path = Path(os.path.join(data_files, "tags.tsv"))
+        data_files = list(data_path.glob("*txt"))
+        tags = self._load_tags(tags_path)
+
+        if self.config.schema == "source":
+            for guid, data_file in enumerate(data_files):
+                document_key = data_file.stem
+                if document_key not in tags:
+                    continue
+                example = self._create_source_example(data_file, tags.get(document_key))
+                example["document_id"] = str(document_key)
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            for guid, data_file in enumerate(data_files):
+                document_key = data_file.stem
+                if document_key not in tags:
+                    continue
+                example = self._create_kb_example(data_file, tags.get(document_key))
+                example["document_id"] = str(document_key)
+                example["id"] = guid
+                yield guid, example
+
+    @staticmethod
+    def _load_tags(path: Path) -> Dict:
+        """Loads all tags into a dictionary with document ID as keys and all annotations to that file as values."""
+        tags = {}
+        document_id_col = 1
+
+        with open(path, encoding="utf-8") as csv_file:
+            reader = csv.reader(csv_file, delimiter="\t")
+            next(reader)
+            for line in reader:
+                document_id = line[document_id_col]
+                line.pop(document_id_col)
+                if document_id not in tags:
+                    tags[document_id] = [line]
+                else:
+                    tags[document_id].append(line)
+        return tags
+
+    def _create_source_example(self, txt_file, tags) -> Dict:
+        """Creates example in source schema."""
+        example = {}
+        example["entities"] = []
+        with open(txt_file, "r") as file:
+            text = file.read()
+        example["text"] = text
+        example["document_type"] = "Article"
+        for tag_id, tag in enumerate(tags):
+            species_id, start, end, entity_text, _ = tag
+            entity_type, db_name, db_id = species_id.split(":")
+            entity = {
+                "id": str(tag_id),
+                "type": entity_type,
+                "text": [entity_text],
+                "offsets": [(int(start), int(end))],
+                "normalized": [
+                    {
+                        "db_name": db_name,
+                        "db_id": db_id,
+                    }
+                ],
+            }
+            example["entities"].append(entity)
+        return example
+
+    def _create_kb_example(self, txt_file, tags) -> Dict:
+        """Creates example in BigBio KB schema."""
+        example = {}
+        with open(txt_file, "r") as file:
+            text = file.read()
+        # Passages
+        example["passages"] = [
+            {
+                "id": f"{txt_file.stem}__text",
+                "text": [text],
+                "type": "Article",
+                "offsets": [(0, len(text))],
+            }
+        ]
+        # Entities
+        example["entities"] = []
+        for tag_id, tag in enumerate(tags):
+            species_id, start, end, entity_text, _ = tag
+            entity_type, db_name, db_id = species_id.split(":")
+            entity = {
+                "id": f"{txt_file.stem}__T{str(tag_id)}",
+                "type": entity_type,
+                "text": [entity_text],
+                "offsets": [(int(start), int(end))],
+                "normalized": [
+                    {
+                        "db_name": db_name,
+                        "db_id": db_id,
+                    }
+                ],
+            }
+            example["entities"].append(entity)
+        example["events"] = []
+        example["relations"] = []
+        example["coreferences"] = []
+        return example