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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 BigBioConfig, Tasks, pairs_features
_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 = "samd"
_DISPLAYNAME = "Sentiment Analysis for Medical Drugs"
_DESCRIPTION = """\
This dataset contains comments about patients and the sentiment in those comments about a specific drug that's \
mentioned.
The dataset has to be download from the Kaggle challenge:
https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment/data
"""
_HOMEPAGE = "https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment"
_LICENSE = "UNKNOWN"
_URLS = {}
_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class SentimentAnalysisMedicalDrugsDatatset(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_pairs",
version=BIGBIO_VERSION,
description=f"{_DATASETNAME} BigBio schema",
schema="bigbio_pairs",
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"),
"label": datasets.Value("string"),
}
)
elif self.config.schema == "bigbio_pairs":
features = pairs_features
else:
raise NotImplementedError(f"Schema {self.config.schema} is not supported")
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 download the data from Kaggle abd pass the directory containing "
"both data files via 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."""
csv_reader = pd.read_csv(filepath, dtype="object")
for _cols, line in csv_reader.iterrows():
if self.config.schema == "source":
document = {
"hash": line["unique_hash"],
"text": line["text"],
"drug_name": line["drug"],
"label": line["sentiment"] if split == "train" else None,
}
yield document["hash"], document
elif self.config.schema == "bigbio_pairs":
document = {
"id": line["unique_hash"],
"document_id": line["unique_hash"],
"text_1": line["text"],
"text_2": line["drug"],
"label": line["sentiment"] if split == "train" else None, # test split labels are not given
}
yield document["id"], document
else:
raise NotImplementedError(f"Schema {self.config.schema} is not supported")
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