upload hubscripts/bc7_litcovid_hub.py to hub from bigbio repo

bc7_litcovid.py

@@ -0,0 +1,215 @@
+# 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 typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{chen2021overview,
+  title        = {
+    Overview of the BioCreative VII LitCovid Track: multi-label topic
+    classification for COVID-19 literature annotation
+  },
+  author       = {
+    Chen, Qingyu and Allot, Alexis and Leaman, Robert and Do{\\u{g}}an, Rezarta
+    Islamaj and Lu, Zhiyong
+  },
+  year         = 2021,
+  booktitle    = {Proceedings of the seventh BioCreative challenge evaluation workshop}
+}
+
+"""
+
+_DATASETNAME = "bc7_litcovid"
+_DISPLAYNAME = "BC7-LitCovid"
+
+_DESCRIPTION = """\
+The training and development datasets contain the publicly-available \
+text of over 30 thousand COVID-19-related articles and their metadata \
+(e.g., title, abstract, journal). Articles in both datasets have been \
+manually reviewed and articles annotated by in-house models.
+"""
+
+_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/"
+
+_LICENSE = 'License information unavailable'
+
+_BASE = "https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/BC7-LitCovid-"
+
+_URLS = {
+    _DATASETNAME: {
+        "train": _BASE + "Train.csv",
+        "validation": _BASE + "Dev.csv",
+        "test": _BASE + "Test-GS.csv",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_CLASS_NAMES = [
+    "Epidemic Forecasting",
+    "Treatment",
+    "Prevention",
+    "Mechanism",
+    "Case Report",
+    "Transmission",
+    "Diagnosis",
+]
+
+
+class BC7LitCovidDataset(datasets.GeneratorBasedBuilder):
+    """
+    Track 5 - LitCovid track Multi-label topic classification for
+    COVID-19 literature annotation
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bc7_litcovid_source",
+            version=SOURCE_VERSION,
+            description="bc7_litcovid source schema",
+            schema="source",
+            subset_id="bc7_litcovid",
+        ),
+        BigBioConfig(
+            name="bc7_litcovid_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bc7_litcovid BigBio schema",
+            schema="bigbio_text",
+            subset_id="bc7_litcovid",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bc7_litcovid_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "journal": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "keywords": datasets.Sequence(datasets.Value("string")),
+                    "pub_type": datasets.Sequence(datasets.Value("string")),
+                    "authors": datasets.Sequence(datasets.Value("string")),
+                    "doi": datasets.Value("string"),
+                    "labels": datasets.Sequence(
+                        datasets.ClassLabel(names=_CLASS_NAMES)
+                    ),
+                }
+            )
+
+        elif self.config.schema == "bigbio_text":
+            features = text_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."""
+
+        # Download all the CSV
+        urls = _URLS[_DATASETNAME]
+        path_train = dl_manager.download(urls["train"])
+        path_validation = dl_manager.download(urls["validation"])
+        path_test = dl_manager.download(urls["test"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path_train,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": path_validation,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": path_test,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        idx = 0
+
+        # Load the CSV and convert it to the string format
+        df = pd.read_csv(filepath, sep=",").astype(str).replace({"nan": None})
+
+        for index, e in df.iterrows():
+
+            if self.config.schema == "source":
+
+                yield idx, {
+                    "pmid": e["pmid"],
+                    "journal": e["journal"],
+                    "title": e["title"],
+                    "abstract": e["abstract"],
+                    "keywords": e["keywords"].split(";")
+                    if e["keywords"] is not None
+                    else [],
+                    "pub_type": e["pub_type"].split(";")
+                    if e["pub_type"] is not None
+                    else [],
+                    "authors": e["authors"].split(";")
+                    if e["authors"] is not None
+                    else [],
+                    "doi": e["doi"],
+                    "labels": e["label"].split(";"),
+                }
+
+            elif self.config.schema == "bigbio_text":
+
+                yield idx, {
+                    "id": idx,
+                    "document_id": e["pmid"],
+                    "text": e["abstract"],
+                    "labels": e["label"].split(";"),
+                }
+
+            idx += 1