"""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, :])