class GWOTestCase(TestCase):
def setUp(self):
self.gwo_custom = GreyWolfOptimizer(10, 20, 1000, MyBenchmark())
self.gwo_sphere = GreyWolfOptimizer(10, 20, 1000, 'sphere')
def test_custom_works_fine(self):
self.assertTrue(self.gwo_custom.run())
def test_sphere_works_fine(self):
self.assertTrue(self.gwo_sphere.run())
class GWOTestCase(TestCase):
def setUp(self):
self.gwo_custom = GreyWolfOptimizer(D=10, NP=20, nFES=1000, benchmark=MyBenchmark())
self.gwo_sphere = GreyWolfOptimizer(NP=10, D=20, nFES=1000, benchmark='sphere')
def test_custom_works_fine(self):
self.assertTrue(self.gwo_custom.run())
def test_sphere_works_fine(self):
self.assertTrue(self.gwo_sphere.run())
# encoding=utf8
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
from NiaPy.algorithms.basic import GreyWolfOptimizer
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Grey Wolf Optimizer for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = GreyWolfOptimizer(NP=40)
best = algo.run(task=task)
print(best)
logging.basicConfig()
logger = logging.getLogger('examples')
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
def __init__(self):
self.Lower = -11
self.Upper = 11
def function(self):
def evaluate(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
return evaluate
for i in range(10):
Algorithm = GreyWolfOptimizer(D=10,
NP=20,
nFES=10000,
benchmark=MyBenchmark())
Best = Algorithm.run()
logger.info(Best)
from NiaPy.algorithms.basic import GreyWolfOptimizer
# we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
for i in range(10):
# first paradeFordPrefect
# meter takes dimension of problem
# second parameter is population size
# third parameter takes the number of function evaluations
# fourth parameter is benchmark function
algorithm = GreyWolfOptimizer(10, 20, 10000, 'pinter')
# running algorithm returns best found minimum
best = algorithm.run()
# printing best minimum
print(best)
class GreyWolfOptimizer(FeatureSelectionAlgorithm):
r"""Implementation of feature selection using GWO algorithm.
Date:
2020
Author:
Luka Pečnik
Reference:
The implementation is adapted according to the following article:
D. Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
Reference URL:
http://iztok-jr-fister.eu/static/publications/236.pdf
License:
MIT
See Also:
* :class:`niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm`
"""
Name = 'Grey Wolf Optimizer'
def __init__(self, **kwargs):
r"""Initialize GWO feature selection algorithm.
"""
super(GreyWolfOptimizer, self).__init__()
self.__gwo = GWO(NP=10)
def __final_output(self, sol):
r"""Calculate final array of features.
Arguments:
sol (numpy.ndarray[float]): Individual of population/ possible solution.
Returns:
numpy.ndarray[bool]: Mask of selected features.
"""
selected = numpy.ones(sol.shape[0] - 1, dtype=bool)
threshold = sol[sol.shape[0] - 1]
for i in range(sol.shape[0] - 1):
if sol[i] < threshold:
selected[i] = False
return selected
def select_features(self, x, y, **kwargs):
r"""Perform the feature selection process.
Arguments:
x (pandas.core.frame.DataFrame): Array of original features.
y (pandas.core.series.Series) Expected classifier results.
Returns:
numpy.ndarray[bool]: Mask of selected features.
"""
num_features = x.shape[1]
benchmark = _FeatureSelectionThresholdBenchmark(x, y)
task = StoppingTask(D=num_features + 1, nFES=1000, benchmark=benchmark)
best = self.__gwo.run(task)
return self.__final_output(benchmark.get_best_solution())
def to_string(self):
r"""User friendly representation of the object.
Returns:
str: User friendly representation of the object.
"""
return FeatureSelectionAlgorithm.to_string(self).format(
name=self.Name,
args=self._parameters_to_string(self.__gwo.getParameters()))
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
from NiaPy.algorithms.basic import GreyWolfOptimizer
from NiaPy.task import StoppingTask
# we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
for i in range(10):
task = StoppingTask(D=10, nFES=1000, benchmark='pinter')
algorithm = GreyWolfOptimizer(NP=20)
best = algorithm.run(task)
print(best)