def test_custom_works_fine(self):
jde_custom = SelfAdaptiveDifferentialEvolution(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark(),
seed=self.seed)
jde_customc = SelfAdaptiveDifferentialEvolution(
D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, jde_custom, jde_customc)
def test_griewank_works_fine(self):
jde_griewank = SelfAdaptiveDifferentialEvolution(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank',
seed=self.seed)
jde_griewankc = SelfAdaptiveDifferentialEvolution(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, jde_griewank, jde_griewankc)
def __init__(self, **kwargs):
r"""Initialize GWO feature selection algorithm.
"""
super(jDEFSTH, self).__init__()
self.__jdefsth = SelfAdaptiveDifferentialEvolution(NP=10,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.5,
Tao2=0.45)
def test_griewank_works_fine(self):
jde_griewank = SelfAdaptiveDifferentialEvolution(NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
jde_griewankc = SelfAdaptiveDifferentialEvolution(NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, jde_griewank, jde_griewankc)
def test_custom_works_fine(self):
jde_custom = SelfAdaptiveDifferentialEvolution(NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
jde_customc = SelfAdaptiveDifferentialEvolution(NP=40,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
seed=self.seed)
AlgorithmTestCase.test_algorithm_run(self, jde_custom, jde_customc,
MyBenchmark())
def test_typeParameters(self):
d = SelfAdaptiveDifferentialEvolution.typeParameters()
self.assertTrue(d['F_l'](10))
self.assertFalse(d['F_l'](-10))
self.assertFalse(d['F_l'](-0))
self.assertTrue(d['F_u'](10))
self.assertFalse(d['F_u'](-10))
self.assertFalse(d['F_u'](-0))
self.assertTrue(d['Tao1'](0.32))
self.assertFalse(d['Tao1'](-1.123))
self.assertFalse(d['Tao1'](1.123))
self.assertTrue(d['Tao2'](0.32))
self.assertFalse(d['Tao2'](-1.123))
self.assertFalse(d['Tao2'](1.123))
# 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.modified import SelfAdaptiveDifferentialEvolution
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Griewank
# we will run jDE algorithm for 5 independent runs
algo = SelfAdaptiveDifferentialEvolution(NP=40, F=0.5, F_l=0.0, F_u=2.0, Tao1=0.9, CR=0.5, Tao2=0.45)
for i in range(5):
task = StoppingTask(D=10, nFES=10000, benchmark=Griewank(Lower=-600, Upper=600), logger=True)
best = algo.run(task)
print('%s -> %s' % (best[0], best[1]))
print(algo.getParameters())
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
# 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
import random
from NiaPy.algorithms.modified import SelfAdaptiveDifferentialEvolution
from NiaPy.util import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
#we will run jDE algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=100, optType=OptimizationType.MINIMIZATION, benchmark=Sphere())
algo = SelfAdaptiveDifferentialEvolution(NP=40, F=0.5, F_l=0.0, F_u=2.0, Tao1=0.9, CR=0.1, Tao2=0.45)
best = algo.run(task=task)
print(best)
class jDEFSTH(FeatureSelectionAlgorithm):
r"""Implementation of self-adaptive differential evolution for feature selection using threshold mechanism.
Date:
2020
Author:
Iztok Fister Jr.
Reference:
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 = 'Self-Adaptive Differential Evolution'
def __init__(self, **kwargs):
r"""Initialize GWO feature selection algorithm.
"""
super(jDEFSTH, self).__init__()
self.__jdefsth = SelfAdaptiveDifferentialEvolution(NP=10,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.5,
Tao2=0.45)
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.__jdefsth.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.__jdefsth.getParameters()))
