Datasets:

bigbio
/

medical_data

Languages:

English

Multilinguality:

monolingual

License:

Dataset card Files Files and versions Community

medical_data / medical_data.py

gabrielaltay

upload hubscripts/medical_data_hub.py to hub from bigbio repo

16c691f over 1 year ago

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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.

	import os
	from typing import Dict, List, Tuple

	import datasets
	import pandas as pd

	from .bigbiohub import entailment_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = False
	_LOCAL = True
	_CITATION = """\
	@misc{ask9medicaldata,
	author = {Khan, Arbaaz},
	title = {Sentiment Analysis for Medical Drugs},
	year = {2019},
	url = {https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment},
	}
	"""

	_DATASETNAME = "medical_data"
	_DISPLAYNAME = "Medical Data"

	_DESCRIPTION = """\
	This dataset is designed to do multiclass classification on medical drugs
	"""

	_HOMEPAGE = ""

	_LICENSE = 'License information unavailable'

	_URLS = {}

	_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]


	_SOURCE_VERSION = "1.0.0"

	_BIGBIO_VERSION = "1.0.0"


	class MedicaldataDatatset(datasets.GeneratorBasedBuilder):
	"""This dataset contains comments about patients and the sentiment in those comments about a specific drug that's mentioned.
	1 - Negative sentiment
	2 - Positive sentiment
	0 - Neutral"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name=f"{_DATASETNAME}_source",
	version=SOURCE_VERSION,
	description=f"{_DATASETNAME} source schema",
	schema="source",
	subset_id=f"{_DATASETNAME}",
	),
	BigBioConfig(
	name=f"{_DATASETNAME}_bigbio_te",
	version=BIGBIO_VERSION,
	description=f"{_DATASETNAME} BigBio schema",
	schema="bigbio_te",
	subset_id=f"{_DATASETNAME}",
	),
	]

	DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":

	features = datasets.Features(
	{
	"hash": datasets.Value("string"),
	"text": datasets.Value("string"),
	"drug_name": datasets.Value("string"),
	"sentiment": datasets.Value("string"),
	}
	)

	elif self.config.schema == "bigbio_te":
	features = entailment_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."""

	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, "train_F3WbcTw.csv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test_tOlRoBf.csv"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
	"""Yields examples as (key, example) tuples."""

	if self.config.schema == "source":
	csv_reader = pd.read_csv(filepath, dtype="object")
	# Train and test splits handled differently as test split is missing values
	if split == "train":
	for _cols, line in csv_reader.iterrows():
	document = {}
	document["hash"] = line["unique_hash"]
	document["text"] = line["text"]
	document["drug_name"] = line["drug"]
	document["sentiment"] = line["sentiment"]
	yield document["hash"], document
	else:
	for _cols, line in csv_reader.iterrows():
	document = {}
	document["hash"] = line["unique_hash"]
	document["text"] = line["text"]
	document["drug_name"] = line["drug"]
	document["sentiment"] = None
	yield document["hash"], document

	elif self.config.schema == "bigbio_te":
	csv_reader = pd.read_csv(filepath, dtype="object")
	# Train and test splits handled differently as test split is missing values
	if split == "train":
	for _cols, line in csv_reader.iterrows():
	document = {}
	document["id"] = line["unique_hash"]
	document["premise"] = line["text"]
	document["hypothesis"] = line["drug"]
	document["label"] = line["sentiment"]
	yield document["id"], document
	else:
	for _cols, line in csv_reader.iterrows():
	document = {}
	document["id"] = line["unique_hash"]
	document["premise"] = line["text"]
	document["hypothesis"] = line["drug"]
	document["label"] = None
	yield document["id"], document