Metadata-Version: 1.2
Name: pykeen
Version: 0.0.19
Summary: A package for training and evaluating knowledge graph embeddings
Home-page: https://github.com/SmartDataAnalytics/PyKEEN.git
Author: Mehdi Ali
Author-email: mehdi.ali@cs.uni-bonn.de
Maintainer: Mehdi Ali
Maintainer-email: mehdi.ali@cs.uni-bonn.de
License: MIT License
Description: PyKEEN |build| |coverage| |docs| |zenodo|
        =========================================
        PyKEEN (Python KnowlEdge EmbeddiNgs) is a package for training and evaluating knowledge graph embeddings. Currently,
        it provides implementations of 10 knowledge graph emebddings models, and can be run in *training mode* in which users
        provide their own set of hyper-parameter values, or in *hyper-parameter optimization mode* to find suitable
        hyper-parameter values from set of user defined values. PyKEEN can also be run without having experience in
        programing by using its interactive command line interface that can be started with the command *pykeen* from a
        terminal.
        
        Installation |pypi_version| |python_versions| |pypi_license|
        ------------------------------------------------------------
        ``pykeen`` can be installed with the following command:
        
        .. code-block:: sh
        
            pip install pykeen
        
        Alternatively, it can be installed from the source for development with:
        
        .. code-block:: sh
        
            $ git clone https://github.com/SmartDataAnalytics/PyKEEN.git pykeen
            $ cd pykeen
            $ pip install -e .
        
        Usage
        -----
        Code examples can be found in the `notebooks directory
        <https://github.com/SmartDataAnalytics/PyKEEN/tree/master/notebooks>`_.
        
        CLI Usage
        ---------
        To start the PyKEEN CLI, run the following command:
        
        .. code-block:: sh
        
            pykeen
        
        then the command line interface will assist you to configure your experiments.
        
        To start PyKEEN with an existing configuration file, run:
        
        .. code-block:: sh
        
            pykeen -c /path/to/config.json
        
        then the command line interface won't be called, instead the pipeline will be started immediately.
        
        Starting the Prediction Pipeline
        ********************************
        To make prediction based on a trained model, run:
        
        .. code-block:: sh
        
            pykeen-predict -m /path/to/model/directory -d /path/to/data/directory
        
        Summarize the Results of All Experiments
        ****************************************
        To summarize the results of all experiments, run:
        
        .. code-block:: sh
        
            pykeen-summarize -d /path/to/experiments/directory -o /path/to/output/file.csv
        
        .. |build| image:: https://travis-ci.org/SmartDataAnalytics/PyKEEN.svg?branch=master
            :target: https://travis-ci.org/SmartDataAnalytics/PyKEEN
            :alt: Build Status
        
        .. |zenodo| image:: https://zenodo.org/badge/136345023.svg
            :target: https://zenodo.org/badge/latestdoi/136345023
            :alt: Zenodo DOI
        
        .. |docs| image:: http://readthedocs.org/projects/pykeen/badge/?version=latest
            :target: https://pykeen.readthedocs.io/en/latest/
            :alt: Documentation Status
        
        .. |python_versions| image:: https://img.shields.io/pypi/pyversions/pykeen.svg
            :alt: Supported Python Versions: 3.6 and 3.7
        
        .. |pypi_version| image:: https://img.shields.io/pypi/v/pykeen.svg
            :alt: Current version on PyPI
        
        .. |pypi_license| image:: https://img.shields.io/pypi/l/pykeen.svg
            :alt: MIT License
        
        .. |coverage| image:: https://codecov.io/gh/SmartDataAnalytics/PyKEEN/branch/master/graphs/badge.svg
            :target: https://codecov.io/gh/SmartDataAnalytics/PyKEEN
            :alt: Coverage Status on CodeCov
        
Keywords: Knowledge Graph Embeddings,Machine Learning,Data Mining,Linked Data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
