Release v.0.1.6

Release notes

• NEW: Hyperparameter search tools - #20, #25, #28
• IMPROVED: Updated structure of Base classes for higher extensibility
• IMPROVED: More robust tests for PlotEnvironment

Hyperparameter Search Tools

PySwarms now implements a native version of GridSearch and RandomSearch to help you find the best hyperparameters in your swarm. To use this feature, simply call the RandomSearch and GridSearch classes from the pyswarms.utils.search module.

import numpy as np
import pyswarms as ps
from pyswarms.utils.search import RandomSearch
from pyswarms.utils.functions import single_obj as fx

# Set-up choices for the parameters
options = {
    'c1': (1,5),
    'c2': (6,10),
    'w': (2,5),
    'k': (11, 15),
    'p': 1
}

# Create a RandomSearch object
# n_selection_iters is the number of iterations to run the searcher
# iters is the number of iterations to run the optimizer

g = RandomSearch(ps.single.LocalBestPSO, n_particles=40,
            dimensions=20, options=options, objective_func=fx.sphere_func,
            iters=10, n_selection_iters=100)

best_score, best_options = g.search()

This then returns the best score found during optimization and the hyperparameter options that enabled it.

>>> best_score
1.41978545901
>>> best_options['c1']
1.543556887693
>>> best_options['c2']
9.504769054771

Improved Library API

Most of the swarm classes now inherit the base class in order to demonstrate its extensibility. If you are a developer or a swarm researcher planning to implement your own algorithms, simply inherit from these Base Classes and implement the optimize() method.