def setParameters(self, F=0.78, CR=0.35, CrossMutt=CrossBest1, **ukwargs):
r"""**__init__(self, D, NP, nFES, A, r, Qmin, Qmax, benchmark)**.
**Arguments:**
F {decimal} -- scaling factor
CR {decimal} -- crossover
"""
BatAlgorithm.setParameters(self, **ukwargs)
self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, F=0.50, CR=0.90, CrossMutt=CrossBest1, **ukwargs):
r"""Set core parameters of HybridBatAlgorithm algorithm.
Arguments:
F (Optional[float]): Scaling factor.
CR (Optional[float]): Crossover.
See Also:
* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
"""
BatAlgorithm.setParameters(self, **ukwargs)
self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
def setParameters(self,
NP=40,
A=0.5,
r=0.5,
Qmin=0.0,
Qmax=2.0,
F=0.78,
CR=0.35,
CrossMutt=CrossBest1,
**ukwargs):
r"""Set core parameters of HybridBatAlgorithm algorithm.
Arguments:
F (Optional[float]): Scaling factor.
CR (Optional[float]): Crossover.
See Also:
* :func:`NiaPy.algorithms.basic.BatAlgorithm.setParameters`
"""
BatAlgorithm.setParameters(self, **ukwargs)
self.A, self.r, self.Qmin, self.Qmax, self.F, self.CR, self.CrossMutt = A, r, Qmin, Qmax, F, CR, CrossMutt
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, **kwargs):
BatAlgorithm.setParameters(self, **kwargs)
self.__setParams(**kwargs)
class BatAlgorithm(FeatureSelectionAlgorithm):
r"""Implementation of feature selection using BA 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 = 'Bat Algorithm'
def __init__(self, **kwargs):
r"""Initialize BA feature selection algorithm.
"""
self._params = dict(A=ParameterDefinition(MinMax(0.5, 1.0),
param_type=float),
r=ParameterDefinition(MinMax(0.0, 0.5),
param_type=float),
Qmin=ParameterDefinition(MinMax(0.0, 1.0),
param_type=float),
Qmax=ParameterDefinition(MinMax(1.0, 2.0),
param_type=float))
self.__ba = BA(NP=10)
def set_parameters(self, **kwargs):
r"""Set the parameters/arguments of the algorithm.
"""
kwargs['NP'] = self.__ba.NP
self.__ba.setParameters(**kwargs)
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:
pandas.core.frame.DataFrame: 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.__ba.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.__ba.getParameters()))
