Update test_dataset.py
Browse files- test_dataset.py +5 -43
test_dataset.py
CHANGED
@@ -56,32 +56,10 @@ _URLS = {
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class NewDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("1.1.0")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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# If you need to make complex sub-parts in the datasets with configurable options
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# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
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datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
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]
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DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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features
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{
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"year": datasets.Value("int32"),
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"locationabbr": datasets.Value("string"),
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@@ -96,30 +74,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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"life_expectancy": datasets.Value("float32")
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# These are the features of your dataset like images, labels ...
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}
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)
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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dl_paths = dl_manager.download_and_extract({
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'train_csv': 'https://drive.google.com/file/d/1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi/view?usp=sharing'
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})
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class NewDataset(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"year": datasets.Value("int32"),
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"locationabbr": datasets.Value("string"),
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"life_expectancy": datasets.Value("float32")
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# These are the features of your dataset like images, labels ...
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}
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),
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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dl_paths = dl_manager.download_and_extract({
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'train_csv': 'https://drive.google.com/file/d/1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi/view?usp=sharing'
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})
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