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 csv
import os
from dataclasses import dataclass
from typing import Dict, List, Tuple

import datasets

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


_DATASETNAME = "testdataset"
_DISPLAYNAME = "TESTDATASET"

_DESCRIPTION = """\
Test Dataset
"""

_CITATION = ""

_HOMEPAGE = "https://www.synapse.org/"

_LICENSE = "other"

_URLS = {}

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


@dataclass
class BigBioConfig(datasets.BuilderConfig):
    """BuilderConfig for BigBio."""

    name: str = None
    version: datasets.Version = None
    description: str = None
    schema: str = None
    subset_id: str = None


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

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="default",
            version=SOURCE_VERSION,
            description="test dataset source schema",
            schema="source",
            subset_id="testdataset",
        ),
        BigBioConfig(
            name="testdataset_te",
            version=BIGBIO_VERSION,
            description="test dataset BigBio schema",
            schema="testdataset_te",
            subset_id="testdataset",
        ),
    ]

    DEFAULT_CONFIG_NAME = "testdataset_source"

    def _info(self) -> datasets.DatasetInfo:
        print(self.config)
        features = datasets.Features(
            {
                "test": datasets.Value("string")
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=str(_LICENSE),
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
        data_dir = self.config.data_dir
        if data_dir is None:
            raise ValueError(
                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
            )

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

    # def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
    #     with open(filepath, "r") as f:
    #         test = csv.reader(f)
    #         i = 0
    #         for row in test:
    #             yield i, row
    #             i = i + 1