kge | knowledge graph embedding library for reproducible research | Machine Learning library

 by   uma-pi1 Python Version: Current License: MIT

kandi X-RAY | kge Summary

kandi X-RAY | kge Summary

kge is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. kge has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

LibKGE is a PyTorch-based library for efficient training, evaluation, and hyperparameter optimization of knowledge graph embeddings (KGE). It is highly configurable, easy to use, and extensible. Other KGE frameworks are listed below. The key goal of LibKGE is to foster reproducible research into (as well as meaningful comparisons between) KGE models and training methods. As we argue in our ICLR 2020 paper (see video), the choice of training strategy and hyperparameters are very influential on model performance, often more so than the model class itself. LibKGE aims to provide clean implementations of training, hyperparameter optimization, and evaluation strategies that can be used with any model. Every potential knob or heuristic implemented in the framework is exposed explicitly via well-documented configuration files (e.g., see here and here). LibKGE also provides the most common KGE models and new ones can be easily added (contributions welcome!). For link prediction tasks, rule-based systems such as AnyBURL are a competitive alternative to KGE.

            kandi-support Support

              kge has a low active ecosystem.
              It has 610 star(s) with 119 fork(s). There are 18 watchers for this library.
              It had no major release in the last 6 months.
              There are 24 open issues and 137 have been closed. On average issues are closed in 4 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of kge is current.

            kandi-Quality Quality

              kge has 0 bugs and 0 code smells.

            kandi-Security Security

              kge has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              kge code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              kge is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              kge releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              kge saves you 3772 person hours of effort in developing the same functionality from scratch.
              It has 8046 lines of code, 456 functions and 62 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed kge and discovered the below as its top functions. This is intended to give you an instant insight into kge implemented functionality, and help decide if they suit your requirements.
            • Evaluate the model
            • Print to console
            • Absolute path to log file
            • Log a message
            • Run the trial
            • Set key value
            • Returns the path to the checkpoint file
            • Check if s is a number
            • Sets up Axes
            • Compute the distance between two embeddings
            • Creates a BCE with the given configuration
            • Create a raw dataset
            • Compute the frequency percentile of each dataset
            • Returns the penalty tensor
            • Perform grid search
            • Process a single subbatch
            • Computes the penalty
            • Import a module
            • Create a KgeModel instance
            • Compute the score of the model
            • Return a list of training entries for training
            • Run training job
            • Run training
            • Score the model
            • Run train job
            • Create an argument parser
            Get all kandi verified functions for this library.

            kge Key Features

            No Key Features are available at this moment for kge.

            kge Examples and Code Snippets

            No Code Snippets are available at this moment for kge.

            Community Discussions


            Prob using axes for subplot an get AttributeError: 'numpy.ndarray' object has no attribute 'plot'
            Asked 2020-Nov-06 at 22:32




            Answered 2020-Nov-06 at 22:32

            As you say, the problem is with this line:



            Selecting specific values in a pandas DataFrame
            Asked 2020-Oct-25 at 14:51

            I have a pandas DataFrame as follow:



            Answered 2020-Oct-25 at 14:51

            Create a flag for each record, so you can use groupby-all easily to determine if all flags are true in groups (sta in your case). Subsequently, rows with qualified sta values can be selected using df.isin().



            How to draw side by side boxplot using facet_wrap in R?
            Asked 2020-Jan-11 at 21:34

            I was looking for a solution to draw side by side boxplot using facet_wrap in R. Though there are lots of good solutions, however, I didn't come across any that i wanted. I decided to draw a picture of the plot that i would like to see of my two data.frame. Data.frame C has my calibration data for the four models of different meterics (i.e., KGE, NSE, PBIAS, and R-Sq) while Data.frame V has my validation data. I want to see a separate plot of each metrics using facet_wrap of the ggplot2 functionality. Below is what i have done so far but its not taking me even closer.



            Answered 2020-Jan-11 at 21:34

            I think you need to split your Variable before plotting in order to have one variable for M1, M2, M3 M4 and one variable for your conditions:



            Namespace dependencies not required error although I've used Imports in DESCRIPTION
            Asked 2019-Aug-11 at 12:00

            I received the following error when running R CMD check:

            Namespace dependencies not required: 'foreach' 'ggplot2' 'magrittr'

            I've found a previous question and follow the answer there by making sure that the packages are included in the Imports field of my DESCRIPTION file, but I still received the error.

            Here is my DESCRIPTION file



            Answered 2018-Nov-30 at 09:58

            When R CMD check runs, it built a binary package .zip file on the parent folder of the package's folder, and also a folder call pkgname.check. I think the next time R CMD check runs, it may not rebuild that folder or that file, depending on whether changes have been made in the package. I deleted those file and folder and rebuild, everything works.



            Why do my button class items share the same lambda functions?
            Asked 2019-Mar-22 at 09:20

            I am trying to make my own reusable button class in SFML. This would allow me to create a button and add a callback function to it in order to make the creation of buttons much easier.

            Here is my hpp file:



            Answered 2019-Mar-22 at 09:20

            The issue was resolved in chat. To summarize, the mouseIsIn function that @iProgram posted did not check collision correctly, leading to multiple buttons triggering at the same time.

            The original function:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install kge

            You can download it from GitHub.
            You can use kge like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            LibKGE currently implements the KGE models listed in features. The examples folder contains some configuration files as examples of how to train these models. We welcome contributions to expand the list of supported models! Please see CONTRIBUTING for details and feel free to initially open an issue.
            Find more information at:

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