| """A dataset script that will hit SQLite file and return the results.""" | |
| import sqlite3 | |
| from pathlib import Path | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _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 new dataset is designed to solve this great NLP task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # 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 = { | |
| "train": "https://huggingface.co/datasets/nateraw/sqllitetest/resolve/main/test.db", | |
| } | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class NewDataset(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| # VERSION = datasets.Version("1.1.0") | |
| # 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"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "ssn": datasets.Value("int32"), | |
| "first_name": datasets.Value("string"), | |
| "last_name": datasets.Value("string"), | |
| "department": datasets.Value("int32"), | |
| "salary": datasets.Value("int32"), | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| db_file = dl_manager.download(_URLS["train"]) | |
| # NOTE - Here is where you would put actual connection details if you're connecting to a database | |
| # For the sake of this example, we use a local path to a sqllite file. | |
| conn = sqlite3.connect(db_file) | |
| # Execute a query and get the cursor object back, which we'll use to iterate over | |
| curr = conn.execute("SELECT * FROM Employees") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"cursor": curr}, | |
| ) | |
| ] | |
| def _generate_examples(self, cursor): | |
| for i, ex in enumerate(cursor): | |
| yield str(i), { | |
| "ssn": ex[0], | |
| "first_name": ex[1], | |
| "last_name": ex[2], | |
| "department": ex[3], | |
| "salary": ex[4], | |
| } | |
| # Probably not necessary but just in case...we close the connection which we can find within the cursor object | |
| cursor.connection.close() | |