StochOPy (STOCHastic OPtimization for PYthon) provides user-friendly routines to sample or optimize objective functions with the most popular algorithms.
Project description
Summary
- Version:
1.2.3
- Author:
Keurfon Luu
- Web site:
- 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]
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
Otherwise, download and extract the package, then run:
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)
References
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.