upload hubscripts/bioasq_task_c_2017_hub.py to hub from bigbio repo

bioasq_task_c_2017.py

@@ -0,0 +1,220 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and
+#
+# 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.
+
+import json
+import os
+import xml.etree.ElementTree as ET
+from dataclasses import dataclass
+from typing import List
+
+import datasets
+
+from .bigbiohub import text.features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = True
+_CITATION = """\
+@article{nentidis-etal-2017-results,
+  title        = {Results of the fifth edition of the {B}io{ASQ} Challenge},
+  author       = {
+    Nentidis, Anastasios  and Bougiatiotis, Konstantinos  and Krithara,
+    Anastasia  and Paliouras, Georgios  and Kakadiaris, Ioannis
+  },
+  year         = 2007,
+  journal      = {},
+  volume       = {BioNLP 2017},
+  doi          = {10.18653/v1/W17-2306},
+  url          = {https://aclanthology.org/W17-2306},
+  biburl       = {},
+  bibsource    = {https://aclanthology.org/W17-2306}
+}
+
+"""
+
+_DATASETNAME = "bioasq_task_c_2017"
+_DISPLAYNAME = "BioASQ Task C 2017"
+
+_DESCRIPTION = """\
+The training data set for this task contains annotated biomedical articles
+published in PubMed and corresponding full text from PMC. By annotated is meant
+that GrantIDs and corresponding Grant Agencies have been identified in the full
+text of articles
+"""
+
+_HOMEPAGE = "http://participants-area.bioasq.org/general_information/Task5c/"
+
+_LICENSE = 'National Library of Medicine Terms and Conditions'
+
+# Website contains all data, but login required
+_URLS = {_DATASETNAME: "http://participants-area.bioasq.org/datasets/"}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+@dataclass
+class BioASQTaskC2017BigBioConfig(BigBioConfig):
+    schema: str = "source"
+    name: str = "bioasq_task_c_2017_source"
+    version: datasets.Version = datasets.Version(_SOURCE_VERSION)
+    description: str = "bioasq_task_c_2017 source schema"
+    subset_id: str = "bioasq_task_c_2017"
+
+
+class BioASQTaskC2017(datasets.GeneratorBasedBuilder):
+    """
+    BioASQ Task C Dataset for 2017
+    """
+
+    DEFAULT_CONFIG_NAME = "bioasq_task_c_2017_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BioASQTaskC2017BigBioConfig(
+            name="bioasq_task_c_2017_source",
+            version=SOURCE_VERSION,
+            description="bioasq_task_c_2017 source schema",
+            schema="source",
+            subset_id="bioasq_task_c_2017",
+        ),
+        BioASQTaskC2017BigBioConfig(
+            name="bioasq_task_c_2017_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bioasq_task_c_2017 BigBio schema",
+            schema="bigbio_text",
+            subset_id="bioasq_task_c_2017",
+        ),
+    ]
+
+    BUILDER_CONFIG_CLASS = BioASQTaskC2017BigBioConfig
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        # BioASQ Task C source schema
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "pmid": datasets.Value("string"),
+                    "pmcid": datasets.Value("string"),
+                    "grantList": [
+                        {
+                            "agency": datasets.Value("string"),
+                        }
+                    ],
+                    "text": datasets.Value("string"),
+                }
+            )
+
+        # For example bigbio_kb, bigbio_t2t
+        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]:
+
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            data_dir = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "taskCTrainingData2017.json"),
+                    "filespath": os.path.join(data_dir, "Train_Text"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "taskc_golden2.json"),
+                    "filespath": os.path.join(data_dir, "Final_Text"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, filespath, split):
+
+        with open(filepath) as f:
+            task_data = json.load(f)
+
+        if self.config.schema == "source":
+            for article in task_data["articles"]:
+
+                with open(filespath + "/" + article["pmcid"] + ".xml") as f:
+                    text = f.read()
+                pmid = article["pmid"]
+
+                yield pmid, {
+                    "text": text,  # articles[pmid],
+                    "document_id": pmid,
+                    "id": str(pmid),
+                    "pmid": pmid,
+                    "pmcid": article["pmcid"],
+                    "grantList": [
+                        {"agency": grant["agency"]} for grant in article["grantList"]
+                    ],
+                }
+
+        elif self.config.schema == "bigbio_text":
+
+            for article in task_data["articles"]:
+
+                with open(filespath + "/" + article["pmcid"] + ".xml") as f:
+                    xml_string = f.read()
+
+                try:
+                    article_body = ET.fromstring(xml_string).find("./article/body")
+                except ET.ParseError:
+
+                    # PubMed XML might not contain namespace which results in parse error, add manually
+                    xml_string = xml_string.replace(
+                        "</pmc-articleset>",
+                        # xlink namespace
+                        '<article xmlns:xlink="http://www.w3.org/1999/xlink"'  # mml namespace
+                        ' xmlns:mml="http://www.w3.org/1998/Math/MathML"'
+                        ' article-type="research-article">',
+                    )
+                    xml_string = xml_string + "</article></pmc-articleset>"
+                    article_body = ET.fromstring(xml_string).find("./article/body")
+
+                text = ET.tostring(article_body, encoding="utf8", method="text")
+
+                yield article["pmid"], {
+                    "text": text,
+                    "id": str(article["pmid"]),
+                    "document_id": article["pmid"],
+                    "labels": [grant["agency"] for grant in article["grantList"]],
+                }