GPflowOpt VS modAL

Compare GPflowOpt vs modAL and see what are their differences.

GPflowOpt

Bayesian Optimization using GPflow (by GPflow)
gpflow bayesian-optimization Machine Learning hyperparameter-optimization
Source Code
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modAL

A modular active learning framework for Python (by modAL-python)
scikit-learn Machine Learning Python active-learning machine-learning-library machine-learning-algorithms machine-learning-api active-learning-module bayesian-optimization
Source Code
modal-python.github.io
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GPflowOpt modAL
1 4
263 2,140
0.0% 1.5%
1.8 1.9
over 3 years ago 2 months ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

GPflowOpt

Posts with mentions or reviews of GPflowOpt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-05.
  • [D] Choosing best parameters from an optimization
    3 projects | /r/MachineLearning | 5 Jun 2021
    1- Hyperparameter optimization as already suggested by u/sener87 but I think your validation does not have to be change as it tests generalization as far as I understand you right. If you have more parameter/larger search space, you may look into Bayesian optimization for this task as implemented e.g. with tensorflow, torch or numpy frameworks.

modAL

Posts with mentions or reviews of modAL. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-12.

What are some alternatives?

When comparing GPflowOpt and modAL you can also consider the following projects:

agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

active_learning - Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.

Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

lightly - A python library for self-supervised learning on images.

pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib

baybe - Bayesian Optimization and Design of Experiments

DataProfiler - What's in your data? Extract schema, statistics and entities from datasets

Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.

DIgging - Decision Intelligence for digging best parameters in target environment.

deep-active-learning - Deep Active Learning

k-nearest-neighbors-algorithm - k-nearest neighbors algorithm (k-NN)

GPflowOpt vs agents modAL vs active_learning GPflowOpt vs Hyperactive modAL vs paramonte modAL vs lightly modAL vs pretty-print-confusion-matrix modAL vs baybe modAL vs DataProfiler modAL vs Encord Active modAL vs DIgging modAL vs deep-active-learning modAL vs k-nearest-neighbors-algorithm