GPflowOpt | Bayesian Optimization using GPflow | Machine Learning library

by GPflow Python Version: v0.1.0 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | GPflowOpt Summary

GPflowOpt is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. GPflowOpt 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.

GPflowOpt is a python package for Bayesian Optimization using GPflow, and uses TensorFlow. It was initiated and is currently maintained by Joachim van der Herten and Ivo Couckuyt. The full list of contributors (in alphabetical order) is Ivo Couckuyt, Tom Dhaene, James Hensman, Nicolas Knudde, Alexander G. de G. Matthews and Joachim van der Herten. Special thanks also to all GPflow contributors as this package would not be able to exist without their effort.

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Quality

Security

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Support

GPflowOpt has a low active ecosystem.

It has 233 star(s) with 57 fork(s). There are 28 watchers for this library.

It had no major release in the last 12 months.

There are 28 open issues and 29 have been closed. On average issues are closed in 75 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of GPflowOpt is v0.1.0

Quality

GPflowOpt has 0 bugs and 0 code smells.

Security

GPflowOpt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

GPflowOpt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GPflowOpt is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

GPflowOpt releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

GPflowOpt saves you 1192 person hours of effort in developing the same functionality from scratch.

It has 2688 lines of code, 378 functions and 33 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed GPflowOpt and discovered the below as its top functions. This is intended to give you an instant insight into GPflowOpt implemented functionality, and help decide if they suit your requirements.

Optimized objective function
Creates an OptimizationResult
The indices of the objective function
Evaluate the acquisition
Builds the acquisition function
Builds the gradient
Builds the prediction for the model
Build the backward variance
Optimize hyperparameters
Append two bounds
Append two values
Returns a DataHolder
Set the value of the variable
Build the acquisition function
Compute the forward gradient
Builds an acquisition
Set the minimum value of the fitness function
Return the indices of the constraints
Setup constraints
Estimate the minimum value for the model
Setup objective function
Setup the objective function
Return an HTML representation of the domain table
Initializes Gumbel sampling
Creates a random design matrix
Returns the feasible data index
Sets up the paretoImprovement method

Get all kandi verified functions for this library.

GPflowOpt Key Features

No Key Features are available at this moment for GPflowOpt.

GPflowOpt Examples and Code Snippets

No Code Snippets are available at this moment for GPflowOpt.

Community Discussions

Trending Discussions on GPflowOpt

Max dimension for Bayesian Optimization (GPyOpt, GPFlow)

QUESTION

Max dimension for Bayesian Optimization (GPyOpt, GPFlow)

Asked 2019-May-07 at 11:06

Does anybody know how quickly the Bayesian Optimization algorithm slows down as a function of the dimension of the search space? What is a good estimate of the maximum dimension that one can reasonably use? I am thinking especially of GPyOpt and GPFlowOpt.

...

ANSWER

Answered 2019-May-07 at 11:06

As a general rule of thumb, Bayesian optimisation becomes ineffective when the dimension of the search space is > 15. This will obviously depend on volume and utility function landscape. Check https://arxiv.org/abs/1902.10675 for an example of bayesian optimisation coupled with some reversible dimensional reduction (an auto-encoder there).

Source https://stackoverflow.com/questions/55537093

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

Vulnerabilities

No vulnerabilities reported

Install GPflowOpt

The easiest way to install GPflowOpt involves cloning this repository and running. in the source directory. This also installs all required dependencies (including TensorFlow, if needed). For more detailed installation instructions, see the documentation.

Support

If you are interested in contributing to this open source project, contact us through an issue on this repository. For more information, see the notes for contributors.

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