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)