upload hubscripts/ehr_rel_hub.py to hub from bigbio repo

ehr_rel.py

@@ -0,0 +1,222 @@
+# 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.
+
+"""
+EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more
+ than the largest existing dataset.  Instead of manually selecting and pairing concepts as done in previous work,
+ the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.
+ A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets.
+"""
+
+import csv
+from pathlib import Path
+from typing import Dict, Iterator, List, Tuple
+
+import datasets
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{schulz-etal-2020-biomedical,
+    title = {Biomedical Concept Relatedness {--} A large {EHR}-based benchmark},
+    author = {Schulz, Claudia  and
+      Levy-Kramer, Josh  and
+      Van Assel, Camille  and
+      Kepes, Miklos  and
+      Hammerla, Nils},
+    booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
+    month = {dec},
+    year = {2020},
+    address = {Barcelona, Spain (Online)},
+    publisher = {International Committee on Computational Linguistics},
+    url = {https://aclanthology.org/2020.coling-main.577},
+    doi = {10.18653/v1/2020.coling-main.577},
+    pages = {6565--6575},
+    }
+"""
+
+_DATASETNAME = "ehr_rel"
+_DISPLAYNAME = "EHR-Rel"
+
+_DESCRIPTION = """\
+EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more
+than the largest existing dataset.  Instead of manually selecting and pairing concepts as done in previous work,
+the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.
+A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets.
+"""
+
+_HOMEPAGE = "https://github.com/babylonhealth/EHR-Rel"
+
+_LICENSE = 'Apache License 2.0'
+
+_URLS = {
+    _DATASETNAME: "https://github.com/babylonhealth/EHR-Rel/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class EHRRelDataset(datasets.GeneratorBasedBuilder):
+    """Dataset for EHR-Rel Corpus"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="ehr_rel_source",
+            version=SOURCE_VERSION,
+            description="EHR-Rel combined source schema",
+            schema="source",
+            subset_id="ehr_rel",
+        ),
+        BigBioConfig(
+            name="ehr_rel_a_source",
+            version=SOURCE_VERSION,
+            description="EHR-Rel-A source schema",
+            schema="source",
+            subset_id="ehr_rel_a",
+        ),
+        BigBioConfig(
+            name="ehr_rel_b_source",
+            version=SOURCE_VERSION,
+            description="EHR-Rel-B source schema",
+            schema="source",
+            subset_id="ehr_rel_b",
+        ),
+        BigBioConfig(
+            name="ehr_rel_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="EHR-Rel BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="ehr_rel",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ehr_rel_bigbio_pairs"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "snomed_id_1": datasets.Value("string"),
+                    "snomed_label_1": datasets.Value("string"),
+                    "snomed_id_2": datasets.Value("string"),
+                    "snomed_label_2": datasets.Value("string"),
+                    "rater_A": datasets.Value("string"),
+                    "rater_B": datasets.Value("string"),
+                    "rater_C": datasets.Value("string"),
+                    "rater_D": datasets.Value("string"),
+                    "rater_E": datasets.Value("string"),
+                    "mean_rating": datasets.Value("string"),
+                    "CUI_1": datasets.Value("string"),
+                    "CUI_2": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_pairs":
+            features = pairs_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 = Path(dl_manager.download_and_extract(urls))
+        data_dir = data_dir.joinpath("EHR-Rel-master")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
+
+        if self.config.subset_id == "ehr_rel_a":
+            files = ["EHR-RelA.tsv"]
+        elif self.config.subset_id == "ehr_rel_b":
+            files = ["EHR-RelB.tsv"]
+        else:
+            files = ["EHR-RelA.tsv", "EHR-RelB.tsv"]
+
+        uid = -1  # want first instance to be 0
+
+        for filename in files:
+            file = data_dir.joinpath(filename)
+            document_id = str(file.stem)
+            with open(file, encoding="utf-8") as csv_file:
+                csv_reader = csv.reader(csv_file, quotechar='"', delimiter="\t")
+                next(csv_reader, None)  # remove column headers
+                for id_, row in enumerate(csv_reader):
+                    uid += 1
+                    (
+                        snomed_id_1,
+                        snomed_label_1,
+                        snomed_id_2,
+                        snomed_label_2,
+                        rater_A,
+                        rater_B,
+                        rater_C,
+                        rater_D,
+                        rater_E,
+                        mean_rating,
+                        CUI_1,
+                        CUI_2,
+                    ) = row
+
+                    if self.config.schema == "source":
+                        yield uid, {
+                            "document_id": document_id,
+                            "snomed_id_1": snomed_id_1,
+                            "snomed_label_1": snomed_label_1,
+                            "snomed_id_2": snomed_id_1,
+                            "snomed_label_2": snomed_label_2,
+                            "rater_A": rater_A,
+                            "rater_B": rater_B,
+                            "rater_C": rater_C,
+                            "rater_D": rater_D,
+                            "rater_E": rater_E,
+                            "mean_rating": mean_rating,
+                            "CUI_1": CUI_1,
+                            "CUI_2": CUI_2,
+                        }
+
+                    elif self.config.schema == "bigbio_pairs":
+                        yield uid, {
+                            "id": uid,
+                            "document_id": document_id,
+                            "text_1": snomed_label_1,
+                            "text_2": snomed_label_2,
+                            "label": mean_rating,
+                        }