Metadata-Version: 2.1
Name: relational-datasets
Version: 0.1.1
Summary: A small library for loading and downloading relational datasets
Home-page: https://srlearn.github.io/relational-datasets/
Author: Alexander L. Hayes
Author-email: alexander@batflyer.net
License: Apache License, Version 2.0
Download-URL: https://github.com/srlearn/relational-datasets
Description: # relational-datasets - (Pre-Alpha Release)
        
        *A small library for loading and downloading relational datasets.*
        
        ```bash
        pip install relational-datasets
        ```
        
        [![PyPi Version](https://img.shields.io/pypi/v/relational-datasets)](https://pypi.org/project/relational-datasets/)
        [![License](https://img.shields.io/github/license/srlearn/relational-datasets)](https://github.com/srlearn/relational-datasets/blob/main/LICENSE)
        [![Python Package Builds](https://github.com/srlearn/relational-datasets/actions/workflows/python-package.yml/badge.svg)](https://github.com/srlearn/relational-datasets/actions/workflows/python-package.yml)
        [![Documentation Deploy](https://github.com/srlearn/relational-datasets/actions/workflows/deploy-docs.yml/badge.svg)](https://github.com/srlearn/relational-datasets/actions/workflows/deploy-docs.yml)
        
        ## Pre-Alpha Release
        
        This API and the datasets at
        [https://github.com/srlearn/datasets/](https://github.com/srlearn/datasets/)
        are currently being experimented with.
        
        Open enhancements and bugs are tracked here:
        
        - [Issues: relational-datasets package](https://github.com/srlearn/relational-datasets/issues)
        - [Issues: datasets](https://github.com/srlearn/datasets/issues)
        
        ## Use Case 1: Fetching Zipfiles
        
        **Running** the `fetch` method downloads a version of a datset to your local cache:
        
        ```python
        import relational_datasets
        
        relational_datasets.fetch("toy_cancer")
        relational_datasets.fetch("toy_father", "v0.0.2")
        relational_datasets.fetch("cora")
        ```
        
        **Resulting in**:
        
        ```console
        ~/relational_datasets/
        ├── toy_cancer_v0.0.3.zip   <--- latest
        ├── toy_father_v0.0.2.zip   <--- specific version
        └── cora_v0.0.3.zip         <--- latest
        ```
        
        ## Use Case 2: Loading Data
        
        The `load` method returns train and test folds—each with `pos`, `neg`, and
        `facts`. Internally it uses `fetch`, so it will automatically download a
        dataset if it is not available.
        
        For example: "*Load fold-2 of webkb*"
        
        ```python
        from relational_datasets import load
        
        train, test = load("webkb", "v0.0.3", fold=2)
        
        print(len(train.facts))
        # 1344
        ```
        
        ## Install
        
        ### From PyPi
        
        ```bash
        pip install relational-datasets
        ```
        
        ### From GitHub Source
        
        ```bash
        git clone https://github.com/srlearn/relational-datasets.git
        cd relational-datasets
        pip install -e .
        ```
        
        ## Contributions
        
        - [Alexander Hayes](https://hayesall.com) - *Indiana University, Bloomington*
        
        This package was partially based on datasets from the
        [Starling Lab Datasets Collection](https://starling.utdallas.edu/datasets/),
        which included specific contributions by
        [Harsha Kokel](https://harshakokel.com/) and
        [Devendra Singh Dhami](https://sites.google.com/view/devendradhami).
        [Tushar Khot](https://allenai.org/team/tushark) converted many to the ILP
        format from Alchemy 2 format, but that occurred before versions were tracked.
        Some inspiration was drawn from the
        "[RelationalDatasets](https://github.com/joschout/RelationalDatasets)" list that
        [Jonas Schouterden](https://people.cs.kuleuven.be/~jonas.schouterden/) collected.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
