{ "info": { "author": "Sujit Rokka Chhetri, Shih-Yuan Yu, Ahmet Salih Aksakal, Palash Goyal, Martinez Canedo, Arquimedes, Mohammad Abdullah Al Faruque", "author_email": "sujitchhetri@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3.6" ], "description": "[![Documentation Status](https://readthedocs.org/projects/pykg2vec/badge/?version=latest)](https://pykg2vec.readthedocs.io/en/latest/?badge=latest) [![CircleCI](https://circleci.com/gh/Sujit-O/pykg2vec.svg?style=svg)](https://circleci.com/gh/Sujit-O/pykg2vec) [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/) [![Build Status](https://travis-ci.org/Sujit-O/pykg2vec.svg?branch=master)](https://travis-ci.org/Sujit-O/pykg2vec) [![PyPI version](https://badge.fury.io/py/pykg2vec.svg)](https://badge.fury.io/py/pykg2vec) [![GitHub license](https://img.shields.io/github/license/Sujit-O/pykg2vec.svg)](https://github.com/Sujit-O/pykg2vec/blob/master/LICENSE) [![Coverage Status](https://coveralls.io/repos/github/Sujit-O/pykg2vec/badge.svg?branch=master)](https://coveralls.io/github/Sujit-O/pykg2vec?branch=master) [![Twitter](https://img.shields.io/twitter/url/https/github.com/Sujit-O/pykg2vec.svg?style=social)](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FSujit-O%2Fpykg2vec) \n\n# Pykg2vec: Python Library for KG Embedding Methods \n## Documentation\nThe documentation of the pykg2vec library is [here](https://pykg2vec.readthedocs.io/). The organization of the readme is as follows,\n* [Introduction](#introdcution)\n* [Dependencies](#dependencies)\n* [Features](#features)\n* [Repository Structure](#repository-structure)\n* [Installation](#installation)\n* [Usage Example](#usage-example)\n* [Common Installation Problems](#common-installation-problems)\n* [How to Contribute?](https://github.com/Sujit-O/pykg2vec/blob/master/CONTRIBUTING.md)\n* [Cite](#cite)\n\n## Introduction\nPykg2vec is a Tensorflow-based library, currently in active development, for learning the representation of entities and relations in Knowledge Graphs. We have attempted to bring all the state-of-the-art knowledge graph embedding algorithms and the necessary building blocks including the whole pipeline into a single library. We hope Pykg2vec is both practical and educational to users who hope to explore the related fields. \n\nFor people who just start working on Knowledge Graph Embedding Methods, the 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) are well-written materials for reading! The figure below illustrates the current overall architecture. \n\n![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/pykg2vec_structure.png?raw=true)\n\n[__***Back to Top***__](#Documentation)\n## Dependencies\nThe goal of this library is to minimize the dependency on other libraries as far as possible to rapidly test the algorithms against different dataset. We emphasize that in the beginning, we will not be focus in run-time performance. However, in the future, may provide faster implementation of each of the algorithms. We encourage installing the tensorflow-gpu version for optimal usage. \n* tensorflow==``\n* networkx>=2.2\n* setuptools>=40.8.0\n* matplotlib>=3.0.3\n* numpy>=1.16.2\n* seaborn>=0.9.0\n* scikit_learn>=0.20.3\n* hyperopt>=0.1.2\n* progressbar2>=3.39.3\n* pathlib>=1.0.1\n\n[__***Back to Top***__](#Documentation)\n## Features\n* A lot of state-of-the-art KGE model implementations and well-known datasets. \n* Tools that support automatic hyperparameter tuning (bayesian optimizer).\n* Optimized performance by making a proper use of CPUs and GPUs (multiprocess and Tensorflow). \n * Will be adding C++ implementation to further optimize! \n* A suite of visualization and summerization tools\n * TSNE based visualization. (Support TSV export)\n * KPI summary visualization (mean rank, hit ratio) in various format. (csvs, figures, latex table)\n\nTraining Loss Plot | Testing Rank Results | Testing Hits Result\n:-------------------------:|:-------------------------:|:-------------------------:\n![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/Freebase15k_training_loss_plot_0-1.png?raw=true) | ![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/Freebase15k_testing_rank_plot_1-1.png?raw=true) | ![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/Freebase15k_testing_hits_plot_1-1.png?raw=true)\n**Relation embedding plot** | **Entity embedding plot** | **Relation and Entity Plot**\n![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/TransE_rel_plot_embedding_plot_0.png?raw=true)| ![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/TransE_entity_plot_embedding_plot_0.png?raw=true) | ![](https://github.com/Sujit-O/pykg2vec/blob/master/figures/TransE_ent_n_rel_plot_embedding_plot_0.png?raw=true)\n\n[__***Back to Top***__](#Documentation)\n## Repository Structure\n\n* **pyKG2Vec/config**: This folder consists of the configuration module. It provides the necessary configuration to parse the datasets, and also consists of the baseline hyperparameters for the knowledge graph embedding algorithms. \n* **pyKG2Vec/core**: This folder consists of the core codes of the knowledge graph embedding algorithms. Inside this folder, each algorithm is implemented as a separate python module. \n* **pyKG2Vec/utils**: This folders consists of modules providing various utilities, such as data preparation, data visualization, and evaluation of the algorithms, data generators, baynesian optimizer.\n* **pyKG2Vec/example**: This folders consists of example codes that can be used to run individual modules or run all the modules at once or tune the model.\n\n[__***Back to Top***__](#Documentation)\n## Installation\n\nFor best performance, we encourage the users to create a virtual environment and setup the necessary dependencies for running the algorithms using Python3.6.\n\n**Please install [tensorflow](https://www.tensorflow.org/install) cpu or gpu version before performing pip install of pykg2vec!**\n\n```bash\n#Prepare your environment\n$ sudo apt update\n$ sudo apt install python3-dev python3-pip\n$ sudo pip3 install -U virtualenv \n\n#Create a virtual environment\n#If you have tensorflow installed in the root env, do the following\n$ virtualenv --system-site-packages -p python3 ./venv\n#If you you want to install tensorflow later, do the following\n$ virtualenv -p python3 ./venv\n\n#Activate the virtual environment using a shell-specific command: \n$ source ./venv/bin/activate\n\n#Upgrade pip: \n$ pip install --upgrade pip\n\n#If you have not installed tensorflow, or not used --system-site-package option while creating venv, install tensorflow first.\n(venv) $ pip install tensorflow\n\n#Install pyKG2Vec: \n(venv) $ pip install pykg2vec\n\n#Install stable version directly from github repo: \n(venv) $ git clone https://github.com/Sujit-O/pykg2vec.git\n(venv) $ cd pykg2vec\n(venv) $ python setup.py install\n\n#Install development version directly from github repo: \n(venv) $ git clone https://github.com/Sujit-O/pykg2vec.git\n(venv) $ cd pykg2vec\n(venv) $ git checkout development\n(venv) $ python setup.py install\n```\n\n[__***Back to Top***__](#Documentation)\n## Usage Example\n\n### Running a single algorithm: \nTrain.py\n```python\nfrom pykg2vec.config.global_config import KnowledgeGraph\nfrom pykg2vec.config.config import Importer, KGEArgParser\nfrom pykg2vec.utils.trainer import Trainer\n\ndef main():\n # getting the customized configurations from the command-line arguments.\n args = KGEArgParser().get_args()\n\n # Preparing data and cache the data for later usage\n knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, negative_sample=args.sampling)\n knowledge_graph.prepare_data()\n\n # Extracting the corresponding model config and definition from Importer(). \n config_def, model_def = Importer().import_model_config(args.model_name.lower())\n config = config_def(args=args)\n model = model_def(config)\n\n # Create, Compile and Train the model. While training, several evaluation will be performed.\n trainer = Trainer(model=model, debug=args.debug)\n trainer.build_model()\n trainer.train_model()\n\n\nif __name__ == \"__main__\":\n main()\n```\nwith train.py we then can train the existed model using command:\n```bach\npython train.py -h # check all tunnable parameters.\npython train.py -mn TransE # Run TransE model.\npython train.py -mn Complex # Run Complex model. \n```\n[__***Back to Top***__](#Documentation)\n\n### Tuning a single algorithm:\ntune_model.py\n```python\n\nfrom pykg2vec.config.hyperparams import KGETuneArgParser\nfrom pykg2vec.utils.bayesian_optimizer import BaysOptimizer\n\ndef main():\n # getting the customized configurations from the command-line arguments.\n args = KGETuneArgParser().get_args()\n\n # initializing bayesian optimizer and prepare data.\n bays_opt = BaysOptimizer(args=args)\n\n # perform the golden hyperparameter tuning. \n bays_opt.optimize()\n\nif __name__ == \"__main__\":\n main()\n``` \nwith tune_model.py we then can train the existed model using command:\n```bach\npython tune_model.py -h # check all tunnable parameters.\npython tune_model.py -mn TransE # Tune TransE model.\n```\n[__***Back to Top***__](#Documentation)\n\n### Switch between Implemented Methods:\nPykg2vec aims to include most of the state-of-the-art KGE methods. You can check [Implemented Algorithms](https://pykg2vec.readthedocs.io/en/latest/algos.html) for more information about the algorithms implemented in pykg2vec. With train.py described in usage examples, you can switch the models to train on a dataset using command:\n```bach\npython train.py -mn TransE # Run TransE model.\npython train.py -mn Complex # Run Complex model. \n# you can select one of models from [\"complex\", \"conve\",\"convkb\",\"hole\", \n \"distmult\", \"kg2e\", \"ntn\": \"NTN\", \n \"proje_pointwise\",\"rescal\",\"rotate\",\n \"slm\",\"sme\",\"transd\",\"TransD\",\n \"transe\",\"transh\",\"transg\",\"transm\",\"transr\",\"tucker\"]\n```\n### Switch between Datasets:\nPykg2vec aims to include all the well-known datasets available online so that you can test all available KGE models or your own model on those datasets. Currently, pykg2vec has [FK15K](https://everest.hds.utc.fr/lib/exe/fetch.php?media=en:fb15k.tgz), WN18, WN18-RR, YAGO, FK15K_237, Kinship, Nations, UMLS. You can check Datasets for more information. \nWith train.py described in usage examples, you can switch the models to train on a dataset using command:\n#### Using Well-Known Dataset \n```bach\npython train.py -mn TransE -ds FB15K # Run TransE model on Freebase15k(FK15)\npython train.py -mn TransE -ds dl50a # Run TransE model on Deeplearning50a(dl50a)\n# you can select one of models from [\"fb15k','dl50a','wn18','wn18_rr',\n 'yago3_10','fb15k_237','ks','nations','umls']\n```\n[__***Back to Top***__](#Documentation)\n\n#### Using Custom Dataset\nFor custom dataset, some steps are provided:\n1. For triples, store all of them in a text-format with each line formatted as follows, \n```\nhead\\trelation\\ttail\n```\n2. For the text file, separate it into three files according to your reference give names as follows, \n```\n[name]-train.txt, [name]-valid.txt, [name]-test.txt\n```\n3. For those three files, create a folder [path_storing_text_files] to include them.\n4. Once finished, you then can use the custom dataset to train on a specific model using command:\n```\npython train.py -mn TransE -ds [name] -dsp [path_storing_text_files] \n# Run TransE model on a custom dataset [name].\n```\n[__***Back to Top***__](#Documentation)\n\n### Perform Inference Tasks:\ninference.py\n```python\nimport sys, code\n\nfrom pykg2vec.utils.kgcontroller import KnowledgeGraph\nfrom pykg2vec.config.config import Importer, KGEArgParser\nfrom pykg2vec.utils.trainer import Trainer\n\ndef main():\n # getting the customized configurations from the command-line arguments.\n args = KGEArgParser().get_args(sys.argv[1:])\n\n # Preparing data and cache the data for later usage\n knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, negative_sample=args.sampling, custom_dataset_path=args.dataset_path)\n knowledge_graph.prepare_data()\n\n # Extracting the corresponding model config and definition from Importer().\n config_def, model_def = Importer().import_model_config(args.model_name.lower())\n config = config_def(args=args)\n model = model_def(config)\n\n # Create, Compile and Train the model. While training, several evaluation will be performed.\n trainer = Trainer(model=model, debug=args.debug)\n trainer.build_model()\n trainer.train_model()\n\n #can perform all the inference here after training the model\n trainer.enter_interactive_mode()\n\n code.interact(local=locals())\n\n trainer.exit_interactive_mode()\n\nif __name__ == \"__main__\":\n main()\n\n```\nFor inference task, you can use the following command: \n```\npython inference.py -mn TransE # train a model on FK15K dataset and enter interactive CMD for manual inference tasks.\npython inference.py -mn TransE -ld true # pykg2vec will look for the location of cached pretrained parameters in your local.\n\n# Once interactive mode is reached, you can execute instruction manually like\n# Example 1: trainer.infer_tails(1,10,topk=5) => give the list of top-5 predicted tails. \n# Example 2: trainer.infer_heads(10,20,topk=5) => give the list of top-5 predicted heads.\n```\n\n[__***Back to Top***__](#Documentation)\n\n## Common Installation Problems\n\n* [SSL: CERTIFICATE_VERIFY_FAILED with urllib](https://stackoverflow.com/questions/49183801/ssl-certificate-verify-failed-with-urllib)\n\n## Cite\n Please kindly cite the paper corresponding to the library. \n\n ```\n @article{yu2019pykg2vec,\n title={Pykg2vec: A Python Library for Knowledge Graph Embedding},\n author={Yu, Shih Yuan and Rokka Chhetri, Sujit and Canedo, Arquimedes and Goyal, Palash and Faruque, Mohammad Abdullah Al},\n journal={arXiv preprint arXiv:1906.04239},\n year={2019}\n}\n\n ```\n[__***Back to Top***__](#Documentation)\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/Sujit-O/pykg2vec.git", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "pykg2vec", "package_url": "https://pypi.org/project/pykg2vec/", "platform": "", "project_url": 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