There is a proverb “You don’t have to reinvent the wheel”. Libraries are the best example of that. It helps you to write complex and time-consuming functionality in an easy way. According to me, a good project uses some of the best libraries available. Optimization is an important process in Machine Learning. It will help you to find the optimal set of parameters for a given model that minimizes the error between the predicted output and the actual output. Here I have compiled 7 useful Python libraries that will help you with Optimization.
This library will help you to optimize the hyperparameters of machine learning models. It is useful for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Currently, it implemented three algorithms Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE. It has more than 6.5k stars on GitHub.
Bayesian optimization is a global optimization algorithm that uses a probabilistic model to trade off exploration and exploitation. It iteratively updates its probabilistic model of the function to be optimized based on the results of previous evaluations and uses this model to choose the next point to evaluate. As you have guessed by name this library will help you to perform Bayesian optimization on black-box functions. It has more than 6.5k stars on GitHub.
POT (Python Optimal Transport) is a Python library for solving optimal transport problems. Optimal transport problems aim to find the best way to map one distribution of points to another distribution of points, such that the cost of mapping is minimized. It provides implementations for various algorithms for solving the optimal transport problem, including the Sinkhorn algorithm, entropic regularization, and more. It also supports various cost functions, such as Euclidean distance, squared Euclidean distance, and the Earth Mover’s Distance. This library has more than 1.5k stars on GitHub.
This library provides a set of scalable optimization algorithms for machine learning and other optimization problems. It includes optimization algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and Evolutionary Algorithms (EA). It also supports parallel processing to speed up optimization. It has more than 3.5k stars on gitHub.
This library will help you to change the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation. It provides a simple interface for defining hyperparameters, conducting experiments, and analyzing results. It supports a variety of search methods, including grid search, random search, and Bayesian optimization. This library has more than 1.5k stars on GitHub.
This is a library for particle swarm optimization (PSO), a type of optimization algorithm based on the social behavior of birds or bees. PSO algorithms are commonly used for solving optimization problems where the objective function is non-linear and/or has multiple local minima. It provides various customization options, such as setting the number of particles, the maximum number of iterations, and the swarm behavior parameters. It has more than 1k stars on GitHub.
This is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unified interface and packaging of several free/open-source nonlinear optimization libraries. The library is written in C and can be used in various programming languages, including Python, through its bindings. It has more than 1k stars on GitHub.
Where are some other awesome resources?
There are always new things to learn. If you want to learn more about awesome resources please check out the below link. That’s all for today. I believe these libraries will help you a lot in your development journey. If you know of any other beautiful Python libraries, please share them in the comments. Until we meet again…Cheers!
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