upload hubscripts/distemist_hub.py to hub from bigbio repo

distemist.py

@@ -0,0 +1,220 @@
+# 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@dataset{luis_gasco_2022_6458455,
+  author       = {Luis Gasco and Eulàlia Farré and Miranda-Escalada, Antonio and Salvador Lima and Martin Krallinger},
+  title        = {{DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases}},
+  month        = apr,
+  year         = 2022,
+  note         = {{Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).}},
+  publisher    = {Zenodo},
+  version      = {2.0.0},
+  doi          = {10.5281/zenodo.6458455},
+  url          = {https://doi.org/10.5281/zenodo.6458455}
+}
+"""
+
+_DATASETNAME = "distemist"
+_DISPLAYNAME = "DisTEMIST"
+
+_DESCRIPTION = """\
+The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.
+All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022.
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/6458455"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "https://zenodo.org/record/6458455/files/distemist.zip?download=1",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "2.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class DistemistDataset(datasets.GeneratorBasedBuilder):
+    """
+    The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT
+    concepts.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="distemist_source",
+            version=SOURCE_VERSION,
+            description="DisTEMIST source schema",
+            schema="source",
+            subset_id="distemist",
+        ),
+        BigBioConfig(
+            name="distemist_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="DisTEMIST BigBio schema",
+            schema="bigbio_kb",
+            subset_id="distemist",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "distemist_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "passages": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "concept_codes": datasets.Sequence(
+                                datasets.Value("string")
+                            ),
+                            "semantic_relations": 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) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "entities_mapping_file_path": Path(data_dir)
+                    / "training/subtrack1_entities/distemist_subtrack1_training_mentions.tsv",
+                    "linking_mapping_file_path": Path(data_dir)
+                    / "training/subtrack2_linking/distemist_subtrack1_training1_linking.tsv",
+                    "text_files_dir": Path(data_dir) / "training/text_files",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        entities_mapping_file_path: Path,
+        linking_mapping_file_path: Path,
+        text_files_dir: Path,
+    ) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        entities_mapping = pd.read_csv(entities_mapping_file_path, sep="\t")
+        linking_mapping = pd.read_csv(linking_mapping_file_path, sep="\t")
+
+        entity_file_names = set(entities_mapping["filename"])
+        linking_file_names = set(linking_mapping["filename"])
+
+        # entity_file_names = entity_file_names.difference(linking_file_names)
+
+        for uid, filename in enumerate(entity_file_names):
+            text_file = text_files_dir / f"{filename}.txt"
+
+            doc_text = text_file.read_text()
+            # doc_text = doc_text.replace("\n", "")
+
+            if filename in linking_file_names:
+                entities_df: pd.DataFrame = linking_mapping[
+                    linking_mapping["filename"] == filename
+                ]
+            else:
+                entities_df: pd.DataFrame = entities_mapping[
+                    entities_mapping["filename"] == filename
+                ]
+
+            example = {
+                "id": f"{uid}",
+                "document_id": filename,
+                "passages": [
+                    {
+                        "id": f"{uid}_{filename}_passage",
+                        "type": "clinical_case",
+                        "text": [doc_text],
+                        "offsets": [[0, len(doc_text)]],
+                    }
+                ],
+            }
+            if self.config.schema == "bigbio_kb":
+                example["events"] = []
+                example["coreferences"] = []
+                example["relations"] = []
+
+            entities = []
+            for row in entities_df.itertuples(name="Entity"):
+                entity = {
+                    "id": f"{uid}_{row.filename}_{row.Index}_entity_id_{row.mark}",
+                    "type": row.label,
+                    "text": [row.span],
+                    "offsets": [[row.off0, row.off1]],
+                }
+                if self.config.schema == "source":
+                    entity["concept_codes"] = []
+                    entity["semantic_relations"] = []
+                    if filename in linking_file_names:
+                        entity["concept_codes"] = row.code.split("+")
+                        entity["semantic_relations"] = row.semantic_rel.split("+")
+
+                elif self.config.schema == "bigbio_kb":
+                    entity["normalized"] = []
+
+                entities.append(entity)
+
+            example["entities"] = entities
+            yield uid, example