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

## Summary

Version:

1.3.1

Author:

Keurfon Luu

Web site:

https://github.com/keurfonluu/stochopy

This document has been placed in the public domain.

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]

• Competitive Particle Swarm Optimization [6]

• Covariance Matrix Adaptation - Evolution Strategy [7]

## Installation

The recommended way to install StochOPy is through pip (internet required):

pip install stochopy

python setup.py install

## 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)

New in 1.3.0: Run StochOPy Viewer to see how popular stochastic algorithm work, and play with the tuning parameters on several benchmark functions.

from stochopy.gui import main

main()

## Project details

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