testdataset.py

# 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.

"""
This is inpsired from the mednil implementation: https://huggingface.co/datasets/bigbio/mednli/blob/main/mednli.py

The files comprising this dataset must be on the users local machine in a single directory that is
passed to `datasets.load_datset` via the `data_dir` kwarg. This loader script will read the archive
files directly (i.e. the user should not uncompress, untar or unzip any of the files). For example,
if `data_dir` is `"testdataset"` it should contain the following files:
testdataset
├── testdataset.zip
"""

import json
import os
from typing import Dict, List, Tuple

import datasets

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


_LANGUAGES = ["English"]
_PUBMED = False
_LOCAL = True


_DATASETNAME = "testdataset"
_DISPLAYNAME = "TESTDATASET"

_DESCRIPTION = """\
Test Dataset
"""


_HOMEPAGE = "https://physionet.org/content/mednli/1.0.0/"

_LICENSE = "PHYSIONET_LICENSE_1p5"

_URLS = {}

# _SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class MedNLIDataset(datasets.GeneratorBasedBuilder):
    """MedNLI"""

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="mednli_source",
            version=SOURCE_VERSION,
            description="MedNLI source schema",
            schema="source",
            subset_id="mednli",
        ),
        BigBioConfig(
            name="mednli_bigbio_te",
            version=BIGBIO_VERSION,
            description="MedNLI BigBio schema",
            schema="bigbio_te",
            subset_id="mednli",
        ),
    ]

    DEFAULT_CONFIG_NAME = "mednli_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "pairID": datasets.Value("string"),
                    "gold_label": datasets.Value("string"),
                    "sentence1": datasets.Value("string"),
                    "sentence2": datasets.Value("string"),
                    "sentence1_parse": datasets.Value("string"),
                    "sentence2_parse": datasets.Value("string"),
                    "sentence1_binary_parse": datasets.Value("string"),
                    "sentence2_binary_parse": 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]:
        if self.config.data_dir is None:
            raise ValueError(
                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
            )
        else:
            extract_dir = dl_manager.extract(
                os.path.join(
                    self.config.data_dir,
                    "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip",
                )
            )
            data_dir = os.path.join(
                extract_dir,
                "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0",
            )

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "mli_train_v1.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "mli_test_v1.jsonl"),
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "mli_dev_v1.jsonl"),
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
        with open(filepath, "r") as f:
            if self.config.schema == "source":
                for line in f:
                    json_line = json.loads(line)
                    yield json_line["pairID"], json_line

            elif self.config.schema == "bigbio_te":
                for line in f:
                    json_line = json.loads(line)
                    entailment_example = {
                        "id": json_line["pairID"],
                        "premise": json_line["sentence1"],
                        "hypothesis": json_line["sentence2"],
                        "label": json_line["gold_label"],
                    }
                    yield json_line["pairID"], entailment_example