test_script.py

"""该脚本是官网下载的模板所改写的"""


# import csv
# import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
import pandas as pd

_CITATION = """
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """
This dataset is a random sample used to learn how to share dataset in the Hub.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "www.huggingface.co/lijianbin"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "123"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }
_URLS = {
    'train': 'https://huggingface.co/datasets/lijianbin/test_script/train.xlsx',
    'validation': 'https://huggingface.co/datasets/lijianbin/test_script/validation.xlsx',
    'test': 'https://huggingface.co/datasets/lijianbin/test_script/test.xlsx',
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class ScriptTest(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
    # ]


    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "label": datasets.Value("float"),
                "x1": datasets.Value("float"),
                "x2": datasets.Value("float"),
                "x3": datasets.Value("float"),
                "x4": datasets.Value("float"),
                "x5": datasets.Value("float"),
                "x6": datasets.Value("float"),
                "x7": datasets.Value("float"),
                "x8": datasets.Value("float"),
                "x9": datasets.Value("float"),
                "x10": datasets.Value("float"),
                # 'shape': (1000, 11)
                # These are the features of your dataset like images, labels ...
            }
        )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS
        data_dir = dl_manager.download_and_extract(urls)
        # data_dir = urls
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir['train']),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir['validation']),
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir['test']),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, 'rb') as f:
            df = pd.read_excel(f)
        for key in df.index.tolist():
            yield key, dict(df.loc[key, :])