Package: mlrose-reborn
mlrose: Machine Learning, Randomized Optimization and SEarchmlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.Project Backgroundmlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.Main FeaturesRandomized Optimization AlgorithmsImplementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;Solve both maximization and minimization problems;Define the algorithm's initial state or start from a random state;Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.Problem TypesSolve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;Define your own fitness function for optimization or use a pre-defined function.Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.Machine Learning Weight OptimizationOptimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;Supports classification and regression neural networks.Installationmlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).The latest version can be installed usingpip:pip install -e git+git://github.com/hiive/mlrose#egg=mlrose-hiiveDocumentationThe official mlrose documentation can be foundhere.A Jupyter notebook containing the examples used in the documentation is also availablehere.Licensing, Authors, Acknowledgementsmlrose was written by Genevieve Hayes and is distributed under the3-Clause BSD license.You can cite mlrose in research publications and reports as follows:Hayes, G. (2019).mlrose: Machine Learning, Randomized Optimization and SEarch package for Python.https://github.com/gkhayes/mlrose. Accessed:day month year.You can cite this fork in a similar way, but please be sure to reference the original work.
Thanks to David S. Park for the MIMIC enhancements (fromhttps://github.com/parkds/mlrose).BibTeX entry:@misc{Hayes19,
 author = {Hayes, G},
 title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
 year 	= 2019,
 howpublished = {\url{https://github.com/gkhayes/mlrose}},
 note 	= {Accessed: day month year}
}