genepy

Python Genetic Algorithm Framework for Parameter Searching

Installation

sudo python setup.py install

Testing

nosetests genepy/

Usage

import genepy.ga
import genepy.generate
import genepy.mutate
import genepy.crossover

import math
import random
import logging

_logger = logging.getLogger(__name__)

_data = None
_slope = 0.0
_offset = 0.0
_noise = 0.0

def generate_data():
	'''generates 1000 data points along a line with noise'''
	global _data
	global _slope
	global _offset
	global _noise
	_slope = round(random.uniform(-100.0, 100.0), 2)
	_offset = round(random.uniform(-100.0, 100.0), 2)
	_noise = random.random()

	_data = []

	_logger.info('generate example data with slope {0}, offset {1}, and noise {2}'.format(_slope, _offset, _noise))
	for i in xrange(0, 1000):
		x = random.random() * 1000.0
		y = _slope * x + _offset + _noise * random.random()
		_data.append((x, y))
	return _data

def fitness(genes):
	global _data

	# create function that outputs estimated line value for x input
	line = lambda x: x * genes['slope'] + genes['offset']

	# measure mean squared error over all data
	mse = 0.0
	for x,y in _data:
		mse += math.pow(y - line(x), 2.0)
	mse /= len(_data)
	_logger.debug('MSE of {0} for slope {1} and offset {2}'.format(mse, genes['slope'], genes['offset']))

	# a better fitness should be a larger number, so take negative of mse as fitness
	return -1.0 * mse

def generation(
	iterations, 
	individuals, 
	genepool, 
	gene_properties, 
	fitness, 
	**kwargs):

	_logger.debug('finished iteration {0}'.format(iterations))
	# get best individual
	best = genepy.ga.best(fitness)
	# check if fitness is better than threshold
	if fitness[best] > -1.0:
		return best
	# return none if we haven't converged
	return None

def run_example():
	generate_data()

	num_individuals = 1000
	gene_properties = {
		'slope': {
			'generate': genepy.generate.sample(random.uniform, -100.0, 100.0),
			'mutate': genepy.mutate.gaussian(10.0),
			'crossover': genepy.crossover.sample_gaussian,
			'process': lambda x: round(x, 2)

		},
		'offset': {
			'generate': genepy.generate.sample(random.uniform, -100.0, 100.0),
			'mutate': genepy.mutate.gaussian(1.0),
			'crossover': genepy.crossover.sample_gaussian,
			'process': lambda x: round(x, 2)
		}
	}

	_logger.info('begin ga search with {0} individuals'.format(num_individuals))

	result = genepy.ga.search(
		num_individuals,
		gene_properties,
		fitness,
		generation,
		mutation_rate=0.3)

	_logger.info('found result {0}'.format(result))
	_logger.info('target slope {0} offset {1}'.format(_slope, _offset))



if __name__ == '__main__':
	logging.basicConfig(level=logging.INFO)
	run_example()