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# 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
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