Metadata-Version: 2.1
Name: pykg2vec
Version: 0.0.52
Summary: A Python library for Knowledge Graph Embedding
Home-page: https://github.com/Sujit-O/pykg2vec.git
Author: Sujit Rokka Chhetri, Shih-Yuan Yu, Ahmet Salih Aksakal, Palash Goyal, Martinez Canedo, Arquimedes, Mohammad Abdullah Al Faruque
Author-email: sujitchhetri@gmail.com
License: UNKNOWN
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        # Pykg2vec: Python Library for KGE Methods 
        Pykg2vec is a library for learning the representation of entities and relations in Knowledge Graphs built on top of PyTorch 1.5 (TF2 version is available in [tf-master](https://github.com/Sujit-O/pykg2vec/tree/tf2-master) branch as well). We have attempted to bring state-of-the-art Knowledge Graph Embedding (KGE) algorithms and the necessary building blocks in the pipeline of knowledge graph embedding task into a single library. We hope Pykg2vec is both practical and educational for people who want to explore the related fields.  
        
        Features:
        * Support state-of-the-art KGE model implementations and benchmark datasets. (also support custom datasets)
        * Support automatic discovery for hyperparameters.
        * Tools for inspecting the learned embeddings. 
          * Support exporting the learned embeddings in TSV or Pandas-supported format.
          * Interactive result inspector.
          * TSNE-based, KPI summary visualization (mean rank, hit ratio) in various format. (csvs, figures, latex table)
        
        ![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/pykg2vec_structure.png?raw=true)
        
        We welcome any form of contribution! Please refer to [CONTRIBUTING.md](https://github.com/Sujit-O/pykg2vec/blob/master/CONTRIBUTING.md) for more details. 
        
        ## To Get Started 
        Before using pykg2vec, we recommend users to have the following libraries installed:
        * python >=3.6 (recommended)
        * pytorch>= 1.5
        
        Quick Guide for Anaconda users:
        
        * Setup a Virtual Environment: we encourage you to use anaconda to work with pykg2vec:
        ```bash
        (base) $ conda create --name pykg2vec python=3.6
        (base) $ conda activate pykg2vec
        ```
        * Setup Pytorch: we encourage to use pytorch with GPU support for good training performance. However, a CPU version also runs. The following sample commands are for setting up pytorch:
        
        ```bash
        # if you have a GPU with CUDA 10.1 installed
        (pykg2vec) $ conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
        # or cpu-only
        (pykg2vec) $ conda install pytorch torchvision cpuonly -c pytorch
        ```
        
        * Setup Pykg2vec:
        ```bash
        (pykg2vec) $ git clone https://github.com/Sujit-O/pykg2vec.git
        (pykg2vec) $ cd pykg2vec
        (pykg2vec) $ python setup.py install
        ```
        
        For beginners, these papers, [A Review of Relational Machine Learning for Knowledge Graphs](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7358050), [Knowledge Graph Embedding: A Survey of Approaches and Applications](https://ieeexplore.ieee.org/document/8047276), and [An overview of embedding models of entities and relationships for knowledge base completion](https://arxiv.org/abs/1703.08098) can be good starting points!
        
        ## User Documentation
        The documentation is [here](https://pykg2vec.readthedocs.io/). 
        
        ## Usage Examples
        With pykg2vec command-line interface, you can
        1. Run a single algorithm with various models and datasets (customized dataset also supported).
            ```
            # Check all tunnable parameters.
            (pykg2vec) $ pykg2vec-train -h
        
            # Train TransE on FB15k benchmark dataset.
            (pykg2vec) $ pykg2vec-train -mn TransE
        
            # Train using different KGE methods.
            (pykg2vec) $ pykg2vec-train -mn [TransE|TransD|TransH|TransG|TransM|TransR|Complex|ComplexN3|
                                CP|RotatE|Analogy|DistMult|KG2E|KG2E_EL|NTN|Rescal|SLM|SME|SME_BL|HoLE|
                                ConvE|ConvKB|Proje_pointwise|MuRP|QuatE|OctonionE|InteractE|HypER]
        
            # For KGE using projection-based loss function, use more processes for batch generation.
            (pykg2vec) $ pykg2vec-train -mn [ConvE|ConvKB|Proje_pointwise] -npg [the number of processes, 4 or 6]
        
            # Train TransE model using different benchmark datasets.
            (pykg2vec) $ pykg2vec-train -mn TransE -ds [fb15k|wn18|wn18_rr|yago3_10|fb15k_237|ks|nations|umls|dl50a|nell_955]
        
            # Train TransE model using your own hyperparameters.
            (pykg2vec) $ pykg2vec-train -exp True -mn TransE -ds fb15k -hpf ./examples/custom_hp.yaml
        
            # Use your own dataset
            (pykg2vec) $ pykg2vec-train -mn TransE -ds [name] -dsp [path to the custom dataset]
            ```
        2. Tune a single algorithm.
            ```
            # Tune TransE using the benchmark dataset.
            (pykg2vec) $ pykg2vec-tune -mn [TransE] -ds [dataset name]
        
            # Tune TransE with your own search space
            (pykg2vec) $ pykg2vec-tune -exp True -mn TransE -ds fb15k -ssf ./examples/custom_ss.yaml
            ```
        3. Perform Inference Tasks (more advanced).
            ```
            # Train a model and perform inference tasks.
            (pykg2vec) $ pykg2vec-infer -mn TransE
        
            # Perform inference tasks over a pretrained model.
            (pykg2vec) $ pykg2vec-infer -mn TransE -ld [path to the pretrained model]
            ```
        \* NB: On Windows, use `pykg2vec-train.exe`, `pykg2vec-tune.exe` and `pykg2vec-infer.exe` instead.
        
        For more usage of pykg2vec APIs, please check the [programming examples](https://pykg2vec.readthedocs.io/en/latest/auto_examples/index.html).
        
        ## Citation
        Please kindly consider citing our paper if you find pykg2vec useful for your research. 
        ```
          @article{yu2019pykg2vec,
          title={Pykg2vec: A Python Library for Knowledge Graph Embedding},
          author={Yu, Shih Yuan and Rokka Chhetri, Sujit and Canedo, Arquimedes and Goyal, Palash and Faruque, Mohammad Abdullah Al},
          journal={arXiv preprint arXiv:1906.04239},
          year={2019}
          }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
