amz20.py

from datasets import Value, ClassLabel,Sequence
import datasets


_AMZ20_CITATION = """\

"""

_AMZ20_DESCRIPTION = """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

class AMZ20Config(datasets.BuilderConfig):

    def __init__(
        self,
        text_features,
        label_column,
        data_url,
        data_dir,
        citation,
        url,
        label_classes=None,
        process_label=lambda x: x,
        **kwargs,
    ):
        super(AMZ20Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
        
        self.text_features = text_features
        self.label_column = label_column
        self.label_classes = label_classes
        self.data_url = data_url
        self.data_dir = data_dir
        self.citation = citation
        self.url = url
        self.process_label = process_label

class AMZ20(datasets.GeneratorBasedBuilder):
    domain_list = ['Sandal', 'Magazine_Subscriptions', 'RiceCooker', 'Flashlight', 'Jewelry', 'CableModem', 'GraphicsCard',
            'GPS', 'Projector', 'Keyboard', 'Video_Games', 'AlarmClock', 'HomeTheaterSystem', 'Vacuum', 'Gloves',
            'Baby', 'Bag', 'Movies_TV', 'Dumbbell', 'Headphone']

    BUILDER_CONFIGS  = [
        AMZ20Config(name=domain_name, 
                    description= f'comments of JD {domain_name}.', 
                    text_features={'sentence':'sentence', 'domain':'domain'}, 
                    label_classes=['POS','NEG', 'NEU'], 
                    label_column='label',
                    citation="",
                    data_dir= "",
                    data_url = "https://huggingface.co/datasets/kuroneko3578/amz20/resolve/main/",
                    url='https://github.com/ws719547997/LNB-DA')
        for domain_name in domain_list
    ]

    def _info(self):
        features = {'id':Value(dtype='int32', id=None),
            'domain':Value(dtype='string', id=None),
            'label':ClassLabel(num_classes=3, names=['POS', 'NEG', 'NEU'], names_file=None, id=None),
            'rank':Value(dtype='int32', id=None),
            'sentence':Value(dtype='string', id=None)}

        return datasets.DatasetInfo(
            description=_AMZ20_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _AMZ20_CITATION,
        )

    def _split_generators(self, dl_manager):

        test_file = rf'{self.config.data_url}test/{self.config.name}.txt'
        dev_file = rf'{self.config.data_url}dev/{self.config.name}.txt'
        train_file = rf'{self.config.data_url}train/{self.config.name}.txt'
        return [datasets.SplitGenerator(name=datasets.Split.TEST,
                                        gen_kwargs={
                                            "data_file": dl_manager.download(test_file),
                                            "split": "test",
                                        },), 
                datasets.SplitGenerator(name=datasets.Split.VALIDATION,
                                        gen_kwargs={
                                            "data_file": dl_manager.download(dev_file),
                                            "split": "dev",
                                        },), 
                datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                        gen_kwargs={
                                            "data_file": dl_manager.download(train_file),
                                            "split": "train",
                                        },)]

    def _generate_examples(self, data_file, split):
        with open(data_file, 'r', encoding='utf-8') as f:
            for line in f:
                lin = line.strip()
                if not lin:
                    continue
                lin_sp = lin.split('\t')
                if len(lin_sp) < 5:
                    continue
                # 列表头也被随机打散到数据集里面了
                if lin_sp[0] == 'index':
                    continue
                # id, {example}
                yield lin_sp[0], {'sentence':lin_sp[4],'domain':lin_sp[1], 'label':lin_sp[2], 'id':lin_sp[0], 'rank':lin_sp[3]}