Metadata-Version: 2.1
Name: pykeen
Version: 0.0.21
Summary: A package for training and evaluating knowledge graph embeddings
Home-page: https://github.com/SmartDataAnalytics/PyKEEN
Author: Mehdi Ali
Author-email: mehdi.ali@cs.uni-bonn.de
Maintainer: Mehdi Ali
Maintainer-email: mehdi.ali@cs.uni-bonn.de
License: MIT
Download-URL: https://github.com/SmartDataAnalytics/PyKEEN/releases
Project-URL: Bug Tracker, https://github.com/SmartDataAnalytics/PyKEEN/issues
Project-URL: Documentation, https://pykeen.readthedocs.io
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 on any system running Python 3.6+ 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 .
        
        However, GPU acceleration is limited to Linux systems with the appropriate graphics cards
        as described in the PyTorch documentation.
        
        Tutorials
        ---------
        Code examples can be found in the `notebooks directory
        <https://github.com/SmartDataAnalytics/PyKEEN/tree/master/notebooks>`_.
        
        CLI Usage - Set Up Your Experiment within 60 seconds
        ----------------------------------------------------
        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
        
        where the value for the argument **-m** is the directory containing the model, in more detail following files must be
        contained in the directory:
        
        * configuration.json
        * entities_to_embeddings.json
        * relations_to_embeddings.json
        * trained_model.pkl
        
        These files are created automatically created after model is trained (and evaluated) and exported in your
        specified output directory.
        
        The value for the argument **-d** is the directory containing the data for which inference should be applied, and it
        needs to contain following files:
        
        * entities.tsv
        * relations.tsv
        
        where *entities.tsv* contains all entities of interest, and relations.tsv all relations. Both files should contain
        should contain a single column containing all the entities/relations. Based on these files, PyKEEN will create all
        triple permutations, and computes the predictions for them, and saves them in data directory
        in *predictions.tsv*.
        Note: the model- and the data-directory can be the same directory as long as all required files are provided.
        
        Optionally, a set of triples can be provided that should be exluded from the prediction, e.g. all the triples
        contained in the training set:
        
        .. code-block:: sh
        
           pykeen-predict -m /path/to/model/directory -d /path/to/data/directory -t /path/to/triples.tsv
        
        Hence, it is easily possible to compute plausibility scores for all triples that are not contained in the training set.
        
        Summarize the Results of All Experiments
        ****************************************
        To summarize the results of all experiments, please provide the path to parent directory containing all the experiments
        as sub-directories, and the path to the output file:
        
        .. 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
Requires-Python: >=3.6
Provides-Extra: docs
Provides-Extra: rdf
Provides-Extra: ndex
