stochopy 1.1.1

StochOPy (STOCHastic OPtimization for PYthon) provides user-friendly routines to sample or optimize objective functions with the most popular algorithms.

StochOPy (STOCHastic OPtimization for PYthon) provides user-friendly routines to sample or optimize objective functions with the most popular algorithms.

Version:

1.1.1

Author:

Keurfon Luu

Web site:

https://github.com/keurfonluu/stochopy

Copyright:

This document has been placed in the public domain.

License:

StochOPy is released under the MIT License.

NOTE: StochOPy has been implemented in the frame of my Ph. D. thesis. If you find any error or bug, or if you have any suggestion, please don’t hesitate to contact me.

Features

StochOPy provides routines for sampling of a model parameter space:

• Pure Monte-Carlo

• Metropolis-Hastings algorithm

• Hamiltonian (Hybrid) Monte-Carlo [1,2]

or optimization of an objective function:

• Differential Evolution [3]

• Particle Swarm Optimization [4,5]

• Covariance Matrix Adaptation - Evolution Strategy [6]

Installation

The recommended way to install StochOPy is through pip:

pip install stochopy

Usage

First, import StochOPy and define an objective function (here Rosenbrock):

import numpy as np
from stochopy import MonteCarlo, Evolutionary

f = lambda x: 100*np.sum((x[1:]-x[:-1]**2)**2)+np.sum((1-x[:-1])**2)

You can define the search space boundaries if necessary:

n_dim = 2
lower = np.full(n_dim, -5.12)
upper = np.full(n_dim, 5.12)

Initialize the Monte-Carlo sampler:

max_iter = 1000
mc = MonteCarlo(f, lower = lower, upper = upper, max_iter = max_iter)

Now, you can start sampling with the simple method ‘sample’:

mc.sample(sampler = "hamiltonian", stepsize = 0.005, n_leap = 20, xstart = [ 2., 2. ])

Note that sampler can be set to “pure” or “hastings” too. The models sampled and their corresponding energies are stored in:

print(mc.models)
print(mc.energy)

Optimization is just as easy:

n_dim = 10
lower = np.full(n_dim, -5.12)
upper = np.full(n_dim, 5.12)
popsize = 4 + np.floor(3.*np.log(n_dim))
ea = Evolutionary(f, lower = lower, upper = upper, popsize = popsize, max_iter = max_iter)
xopt, gfit = ea.optimize(solver = "cmaes")
print(xopt)
print(gfit)

References

[1]

S. Duane, A. D. Kennedy, B. J. Pendleton and D. Roweth, Hybrid Monte Carlo, Physics Letters B., 1987, 195(2): 216-222

[2]

N. Radford, MCMC Using Hamiltonian Dynamics, Handbook of Markov Chain Monte Carlo, Chapman and Hall/CRC, 2011

[3]

R. Storn and K. Price, Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11(4): 341-359

[4]

J. Kennedy and R. Eberhart, Particle swarm optimization, Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, 4: 1942-1948

[5]

F. Van Den Bergh, An analysis of particle swarm optimizers, University of Pretoria, 2001

[6]

N. Hansen, The CMA evolution strategy: A tutorial, Inria, Université Paris-Saclay, LRI, 2011, 102: 1-34