upload hubscripts/pmc_patients_hub.py to hub from bigbio repo

pmc_patients.py

@@ -0,0 +1,207 @@
+# 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.
+
+"""
+PPS dataset is a list of triplets. Each entry is in format (patient_uid_1, patient_uid_2, similarity)
+where similarity has three values:0, 1, 2, indicating corresponding similarity.
+"""
+
+import json
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@misc{zhao2022pmcpatients,
+      title={PMC-Patients: A Large-scale Dataset of Patient Notes and Relations Extracted from Case
+          Reports in PubMed Central},
+      author={Zhengyun Zhao and Qiao Jin and Sheng Yu},
+      year={2022},
+      eprint={2202.13876},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_DATASETNAME = "pmc_patients"
+_DISPLAYNAME = "PMC-Patients"
+
+_DESCRIPTION = """\
+This dataset is used for calculating the similarity between two patient descriptions.
+"""
+
+_HOMEPAGE = "https://github.com/zhao-zy15/PMC-Patients"
+
+_LICENSE = 'Creative Commons Attribution Non Commercial Share Alike 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "https://drive.google.com/u/0/uc?id=1vFCLy_CF8fxPDZvDtHPR6Dl6x9l0TyvW&export=download",
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+_SOURCE_VERSION = "1.2.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class PMCPatientsDataset(datasets.GeneratorBasedBuilder):
+    """PPS dataset is a list of triplets.
+    Each entry is in format (patient_uid_1, patient_uid_2, similarity) and their
+    respective texts.
+    where similarity has three values:0, 1, 2, indicating corresponding similarity.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="pmc_patients_source",
+            version=SOURCE_VERSION,
+            description="pmc_patients source schema",
+            schema="source",
+            subset_id="pmc_patients",
+        ),
+        BigBioConfig(
+            name="pmc_patients_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="pmc_patients BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="pmc_patients",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "pmc_patients_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "id_text1": datasets.Value("string"),
+                    "id_text2": datasets.Value("string"),
+                    "label": datasets.Value("int8"),
+                }
+            )
+
+        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 = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir,
+                        "datasets/task_2_patient2patient_similarity/PPS_train.json",
+                    ),
+                    "split": "train",
+                    "data_dir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir,
+                        "datasets/task_2_patient2patient_similarity/PPS_test.json",
+                    ),
+                    "split": "test",
+                    "data_dir": data_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir,
+                        "datasets/task_2_patient2patient_similarity/PPS_dev.json",
+                    ),
+                    "split": "dev",
+                    "data_dir": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath, split: str, data_dir: str
+    ) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        uid = 0
+
+        def lookup_text(patient_uid: str, df: pd.DataFrame) -> str:
+            try:
+                return df.loc[patient_uid]["patient"]
+            except KeyError:
+                return ""
+
+        with open(filepath, "r") as j:
+            ret_file = json.load(j)
+
+        if self.config.schema == "source":
+
+            for key, (id1, id2, label) in enumerate(ret_file):
+                feature_dict = {
+                    "id": uid,
+                    "id_text1": id1,
+                    "id_text2": id2,
+                    "label": label,
+                }
+                uid += 1
+                yield key, feature_dict
+
+        elif self.config.schema == "bigbio_pairs":
+            source_files = os.path.join(data_dir, f"datasets/PMC-Patients_{split}.json")
+            src_frame = pd.read_json(source_files, encoding="utf8").set_index(
+                "patient_uid"
+            )
+            for key, (id1, id2, label) in enumerate(ret_file):
+                text_1 = lookup_text(id1, src_frame)
+                text_2 = lookup_text(id2, src_frame)
+                # test/dev splits are faulty and may not contain the patient_uid
+                # if any of the lookup texts are empty skip the sample
+                if text_1 == "" or text_2 == "":
+                    continue
+                feature_dict = {
+                    "id": uid,
+                    "document_id": "NULL",
+                    "text_1": text_1,
+                    "text_2": text_2,
+                    "label": label,
+                }
+                uid += 1
+                yield key, feature_dict