NiaPy.algorithms.basic.foa.ForestOptimizationAlgorithm.setParameters() - Code Metrics - Inspection of "FOA tree aging and limitRepair bug fix." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#205)

unknown

created 2019-04-19 16:50 UTC

ForestOptimizationAlgorithm.setParameters() A

↳ Parent: NiaPy.algorithms.basic.foa

Complexity

Conditions

Size

Total Lines	18
Code Lines	4

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	4
nop	8
dl	0
loc	18
rs	10
c	0
b	0
f	0

How to fix Many Parameters

# encoding=utf8
# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, redefined-builtin, line-too-long, no-self-use, arguments-differ, no-else-return, bad-continuation
import logging
from numpy import where, apply_along_axis, zeros, append, ndarray, delete, arange, argmin, absolute, int32
from NiaPy.algorithms.algorithm import Algorithm
from NiaPy.util import limitRepair

__all__ = ['ForestOptimizationAlgorithm']

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')

class ForestOptimizationAlgorithm(Algorithm):
    r"""Implementation of Forest Optimization Algorithm.

    Algorithm:
        Forest Optimization Algorithm

    Date:
        2019

    Authors:
        Luka Pečnik

    License:
        MIT

    Reference paper:
        Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.

    References URL:
        Implementation is based on the following MATLAB code: https://github.com/cominsys/FOA

    Attributes:
        Name (List[str]): List of strings representing algorithm name.
        lt (int): Life time of trees parameter.
        al (int): Area limit parameter.
        lsc (int): Local seeding changes parameter.
        gsc (int): Global seeding changes parameter.
        tr (float): Transfer rate parameter.

    See Also:
        * :class:`NiaPy.algorithms.Algorithm`
    """
    Name = ['ForestOptimizationAlgorithm', 'FOA']

    @staticmethod
    def algorithmInfo():
        return r"""
        Description: Forest Optimization Algorithm is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period.
        Authors: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi
        Year: 2014
    Main reference: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
    """

    @staticmethod

    def typeParameters():
        r"""Get dictionary with functions for checking values of parameters.

        Returns:
            Dict[str, Callable]:
                * lt (Callable[[int], bool]): Checks if life time parameter has a proper value.
                * al (Callable[[int], bool]): Checks if area limit parameter has a proper value.
                * lsc (Callable[[int], bool]): Checks if local seeding changes parameter has a proper value.
                * gsc (Callable[[int], bool]): Checks if global seeding changes parameter has a proper value.
                * tr (Callable[[float], bool]): Checks if transfer rate parameter has a proper value.

        See Also:
            * :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
        """
        d = Algorithm.typeParameters()
        d.update({
            'lt': lambda x: isinstance(x, int) and x > 0,
            'al': lambda x: isinstance(x, int) and x > 0,
            'lsc': lambda x: isinstance(x, int) and x > 0,
            'gsc': lambda x: isinstance(x, int) and x > 0,
            'tr': lambda x: isinstance(x, float) and 0 <= x <= 1,
        })
        return d

    def setParameters(self, NP=10, lt=3, al=10, lsc=1, gsc=1, tr=0.3, **ukwargs):
        r"""Set the parameters of the algorithm.

        Args:
            NP (Optional[int]): Population size.
            lt (Optional[int]): Life time parameter.
            al (Optional[int]): Area limit parameter.
            lsc (Optional[int]): Local seeding changes parameter.
            gsc (Optional[int]): Global seeding changes parameter.
            tr (Optional[float]): Transfer rate parameter.
            ukwargs (Dict[str, Any]): Additional arguments.

        See Also:
            * :func:`NiaPy.algorithms.Algorithm.setParameters`
        """
        Algorithm.setParameters(self, NP=NP)
        self.lt, self.al, self.lsc, self.gsc, self.tr = lt, al, lsc, gsc, tr
        if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

    def localSeeding(self, task, trees):
        r"""Local optimum search stage.

        Args:
            task (Task): Optimization task.
            trees (numpy.ndarray): Zero age trees for local seeding.

        Returns:
            numpy.ndarray: Resulting zero age trees.
        """
        n = trees.shape[0]
        deltas = self.uniform(-self.dx, self.dx, (n, self.lsc))
        deltas = append(deltas, zeros((n, task.D - self.lsc)), axis=1)
        perms = self.rand([deltas.shape[0], deltas.shape[1]]).argsort(1)
        deltas = deltas[arange(deltas.shape[0])[:, None], perms]
        trees += deltas
        trees = apply_along_axis(limitRepair, 1, trees, task.Lower, task.Upper)
        return trees

    def globalSeeding(self, task, candidates, size):
        r"""Global optimum search stage that should prevent getting stuck in a local optimum.

        Args:
            task (Task): Optimization task.
            candidates (numpy.ndarray): Candidate population for global seeding.
            size (int): Number of trees to produce.

        Returns:
            numpy.ndarray: Resulting trees.
        """
        seeds = candidates[self.randint(len(candidates), D=size)]
        deltas = self.uniform(task.benchmark.Lower, task.benchmark.Upper, (size, self.gsc))
        deltas = append(deltas, zeros((size, task.D - self.gsc)), axis=1)
        perms = self.rand([deltas.shape[0], deltas.shape[1]]).argsort(1)
        deltas = deltas[arange(deltas.shape[0])[:, None], perms]

        deltas = deltas.flatten()
        seeds = seeds.flatten()
        seeds[deltas != 0] = deltas[deltas != 0]

        return seeds.reshape(size, task.D)

    def removeLifeTimeExceeded(self, trees, candidates, age):
        r"""Remove dead trees.

        Args:
            trees (numpy.ndarray): Population to test.
            candidates (numpy.ndarray): Candidate population array to be updated.
            age (numpy.ndarray[int32]): Age of trees.

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray[int32]]:
                1. Alive trees.
                2. New candidate population.
                3. Age of trees.
        """
        lifeTimeExceeded = where(age > self.lt)
        candidates = trees[lifeTimeExceeded]
        trees = delete(trees, lifeTimeExceeded, axis=0)
        age = delete(age, lifeTimeExceeded, axis=0)
        return trees, candidates, age

    def survivalOfTheFittest(self, task, trees, candidates, age):
        r"""Evaluate and filter current population.

        Args:
            task (Task): Optimization task.
            trees (numpy.ndarray): Population to evaluate.
            candidates (numpy.ndarray): Candidate population array to be updated.
            age (numpy.ndarray[int32]): Age of trees.

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray[float], numpy.ndarray[int32]]:
                1. Trees sorted by fitness value.
                2. Updated candidate population.
                3. Population fitness values.
                4. Age of trees
        """
        evaluations = apply_along_axis(task.eval, 1, trees)
        ei = evaluations.argsort()
        candidates = append(candidates, trees[ei[self.al:]], axis=0)
        trees = trees[ei[:self.al]]
        age = age[ei[:self.al]]
        evaluations = evaluations[ei[:self.al]]
        return trees, candidates, evaluations, age

    def initPopulation(self, task):
        r"""Initialize the starting population.

        Args:
            task (Task): Optimization task

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
                1. New population.
                2. New population fitness/function values.
                3. Additional arguments:
                    * age (numpy.ndarray[int32]): Age of trees.

        See Also:
            * :func:`NiaPy.algorithms.Algorithm.initPopulation`
        """
        Trees, Evaluations, _ = Algorithm.initPopulation(self, task)
        age = zeros(self.NP, dtype=int32)
        self.dx = absolute(task.benchmark.Upper) / 5
        return Trees, Evaluations, {'age': age}

    def runIteration(self, task, Trees, Evaluations, xb, fxb, age, **dparams):
        r"""Core function of Forest Optimization Algorithm.

        Args:
            task (Task): Optimization task.
            Trees (numpy.ndarray): Current population.
            Evaluations (numpy.ndarray[float]): Current population function/fitness values.
            xb (numpy.ndarray): Global best individual.
            fxb (float): Global best individual fitness/function value.
            age (numpy.ndarray[int32]): Age of trees.
            **dparams (Dict[str, Any]): Additional arguments.

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
                1. New population.
                2. New population fitness/function values.
                3. Additional arguments:
                    * age (numpy.ndarray[int32]): Age of trees.
        """
        candidatePopulation = ndarray((0, task.D + 1))
        zeroAgeTrees = Trees[age == 0]

        localSeeds = self.localSeeding(task, zeroAgeTrees)
        age += 1

        Trees, candidatePopulation, age = self.removeLifeTimeExceeded(Trees, candidatePopulation, age)
        Trees = append(Trees, localSeeds, axis=0)
        age = append(age, zeros(len(localSeeds), dtype=int32))
        Trees, candidatePopulation, Evaluations, age = self.survivalOfTheFittest(task, Trees, candidatePopulation, age)

        gsn = int(self.tr * len(candidatePopulation))
        if gsn > 0:
            globalSeeds = self.globalSeeding(task, candidatePopulation, gsn)
            Trees = append(Trees, globalSeeds, axis=0)
            age = append(age, zeros(len(globalSeeds), dtype=int32))
            gste = apply_along_axis(task.eval, 1, globalSeeds)
            Evaluations = append(Evaluations, gste)

        ib = argmin(Evaluations)
        age[ib] = 0
        return Trees, Evaluations, {'age': age}


1		# encoding=utf8
2		# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, redefined-builtin, line-too-long, no-self-use, arguments-differ, no-else-return, bad-continuation
3		import logging
4		from numpy import where, apply_along_axis, zeros, append, ndarray, delete, arange, argmin, absolute, int32
5		from NiaPy.algorithms.algorithm import Algorithm
6		from NiaPy.util import limitRepair
7
8		__all__ = ['ForestOptimizationAlgorithm']
9
10		logging.basicConfig()
11		logger = logging.getLogger('NiaPy.algorithms.basic')
12		logger.setLevel('INFO')
13
14		class ForestOptimizationAlgorithm(Algorithm):
15		r"""Implementation of Forest Optimization Algorithm.
16
17		Algorithm:
18		Forest Optimization Algorithm
19
20		Date:
21		2019
22
23		Authors:
24		Luka Pečnik
25
26		License:
27		MIT
28
29		Reference paper:
30		Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
31
32		References URL:
33		Implementation is based on the following MATLAB code: https://github.com/cominsys/FOA
34
35		Attributes:
36		Name (List[str]): List of strings representing algorithm name.
37		lt (int): Life time of trees parameter.
38		al (int): Area limit parameter.
39		lsc (int): Local seeding changes parameter.
40		gsc (int): Global seeding changes parameter.
41		tr (float): Transfer rate parameter.
42
43		See Also:
44		* :class:`NiaPy.algorithms.Algorithm`
45		"""
46		Name = ['ForestOptimizationAlgorithm', 'FOA']
47
48		@staticmethod
49		def algorithmInfo():
50		return r"""
51		Description: Forest Optimization Algorithm is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period.
52		Authors: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi
53		Year: 2014
54		Main reference: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, Forest Optimization Algorithm, Expert Systems with Applications, Volume 41, Issue 15, 2014, Pages 6676-6687, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.05.009.
55		"""
56
57	View Code Duplication	@staticmethod
		0 ignored issues – show Duplication introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
58		def typeParameters():
59		r"""Get dictionary with functions for checking values of parameters.
60
61		Returns:
62		Dict[str, Callable]:
63		* lt (Callable[[int], bool]): Checks if life time parameter has a proper value.
64		* al (Callable[[int], bool]): Checks if area limit parameter has a proper value.
65		* lsc (Callable[[int], bool]): Checks if local seeding changes parameter has a proper value.
66		* gsc (Callable[[int], bool]): Checks if global seeding changes parameter has a proper value.
67		* tr (Callable[[float], bool]): Checks if transfer rate parameter has a proper value.
68
69		See Also:
70		* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
71		"""
72		d = Algorithm.typeParameters()
73		d.update({
74		'lt': lambda x: isinstance(x, int) and x > 0,
75		'al': lambda x: isinstance(x, int) and x > 0,
76		'lsc': lambda x: isinstance(x, int) and x > 0,
77		'gsc': lambda x: isinstance(x, int) and x > 0,
78		'tr': lambda x: isinstance(x, float) and 0 <= x <= 1,
79		})
80		return d
81
82		def setParameters(self, NP=10, lt=3, al=10, lsc=1, gsc=1, tr=0.3, **ukwargs):
83		r"""Set the parameters of the algorithm.
84
85		Args:
86		NP (Optional[int]): Population size.
87		lt (Optional[int]): Life time parameter.
88		al (Optional[int]): Area limit parameter.
89		lsc (Optional[int]): Local seeding changes parameter.
90		gsc (Optional[int]): Global seeding changes parameter.
91		tr (Optional[float]): Transfer rate parameter.
92		ukwargs (Dict[str, Any]): Additional arguments.
93
94		See Also:
95		* :func:`NiaPy.algorithms.Algorithm.setParameters`
96		"""
97		Algorithm.setParameters(self, NP=NP)
98		self.lt, self.al, self.lsc, self.gsc, self.tr = lt, al, lsc, gsc, tr
99		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
100
101		def localSeeding(self, task, trees):
102		r"""Local optimum search stage.
103
104		Args:
105		task (Task): Optimization task.
106		trees (numpy.ndarray): Zero age trees for local seeding.
107
108		Returns:
109		numpy.ndarray: Resulting zero age trees.
110		"""
111		n = trees.shape[0]
112		deltas = self.uniform(-self.dx, self.dx, (n, self.lsc))
113		deltas = append(deltas, zeros((n, task.D - self.lsc)), axis=1)
114		perms = self.rand([deltas.shape[0], deltas.shape[1]]).argsort(1)
115		deltas = deltas[arange(deltas.shape[0])[:, None], perms]
116		trees += deltas
117		trees = apply_along_axis(limitRepair, 1, trees, task.Lower, task.Upper)
118		return trees
119
120		def globalSeeding(self, task, candidates, size):
121		r"""Global optimum search stage that should prevent getting stuck in a local optimum.
122
123		Args:
124		task (Task): Optimization task.
125		candidates (numpy.ndarray): Candidate population for global seeding.
126		size (int): Number of trees to produce.
127
128		Returns:
129		numpy.ndarray: Resulting trees.
130		"""
131		seeds = candidates[self.randint(len(candidates), D=size)]
132		deltas = self.uniform(task.benchmark.Lower, task.benchmark.Upper, (size, self.gsc))
133		deltas = append(deltas, zeros((size, task.D - self.gsc)), axis=1)
134		perms = self.rand([deltas.shape[0], deltas.shape[1]]).argsort(1)
135		deltas = deltas[arange(deltas.shape[0])[:, None], perms]
136
137		deltas = deltas.flatten()
138		seeds = seeds.flatten()
139		seeds[deltas != 0] = deltas[deltas != 0]
140
141		return seeds.reshape(size, task.D)
142
143		def removeLifeTimeExceeded(self, trees, candidates, age):
144		r"""Remove dead trees.
145
146		Args:
147		trees (numpy.ndarray): Population to test.
148		candidates (numpy.ndarray): Candidate population array to be updated.
149		age (numpy.ndarray[int32]): Age of trees.
150
151		Returns:
152		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray[int32]]:
153		1. Alive trees.
154		2. New candidate population.
155		3. Age of trees.
156		"""
157		lifeTimeExceeded = where(age > self.lt)
158		candidates = trees[lifeTimeExceeded]
159		trees = delete(trees, lifeTimeExceeded, axis=0)
160		age = delete(age, lifeTimeExceeded, axis=0)
161		return trees, candidates, age
162
163		def survivalOfTheFittest(self, task, trees, candidates, age):
164		r"""Evaluate and filter current population.
165
166		Args:
167		task (Task): Optimization task.
168		trees (numpy.ndarray): Population to evaluate.
169		candidates (numpy.ndarray): Candidate population array to be updated.
170		age (numpy.ndarray[int32]): Age of trees.
171
172		Returns:
173		Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray[float], numpy.ndarray[int32]]:
174		1. Trees sorted by fitness value.
175		2. Updated candidate population.
176		3. Population fitness values.
177		4. Age of trees
178		"""
179		evaluations = apply_along_axis(task.eval, 1, trees)
180		ei = evaluations.argsort()
181		candidates = append(candidates, trees[ei[self.al:]], axis=0)
182		trees = trees[ei[:self.al]]
183		age = age[ei[:self.al]]
184		evaluations = evaluations[ei[:self.al]]
185		return trees, candidates, evaluations, age
186
187		def initPopulation(self, task):
188		r"""Initialize the starting population.
189
190		Args:
191		task (Task): Optimization task
192
193		Returns:
194		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
195		1. New population.
196		2. New population fitness/function values.
197		3. Additional arguments:
198		* age (numpy.ndarray[int32]): Age of trees.
199
200		See Also:
201		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
202		"""
203		Trees, Evaluations, _ = Algorithm.initPopulation(self, task)
204		age = zeros(self.NP, dtype=int32)
205		self.dx = absolute(task.benchmark.Upper) / 5
206		return Trees, Evaluations, {'age': age}
207
208		def runIteration(self, task, Trees, Evaluations, xb, fxb, age, **dparams):
209		r"""Core function of Forest Optimization Algorithm.
210
211		Args:
212		task (Task): Optimization task.
213		Trees (numpy.ndarray): Current population.
214		Evaluations (numpy.ndarray[float]): Current population function/fitness values.
215		xb (numpy.ndarray): Global best individual.
216		fxb (float): Global best individual fitness/function value.
217		age (numpy.ndarray[int32]): Age of trees.
218		**dparams (Dict[str, Any]): Additional arguments.
219
220		Returns:
221		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
222		1. New population.
223		2. New population fitness/function values.
224		3. Additional arguments:
225		* age (numpy.ndarray[int32]): Age of trees.
226		"""
227		candidatePopulation = ndarray((0, task.D + 1))
228		zeroAgeTrees = Trees[age == 0]
229
230		localSeeds = self.localSeeding(task, zeroAgeTrees)
231		age += 1
232
233		Trees, candidatePopulation, age = self.removeLifeTimeExceeded(Trees, candidatePopulation, age)
234		Trees = append(Trees, localSeeds, axis=0)
235		age = append(age, zeros(len(localSeeds), dtype=int32))
236		Trees, candidatePopulation, Evaluations, age = self.survivalOfTheFittest(task, Trees, candidatePopulation, age)
237
238		gsn = int(self.tr * len(candidatePopulation))
239		if gsn > 0:
240		globalSeeds = self.globalSeeding(task, candidatePopulation, gsn)
241		Trees = append(Trees, globalSeeds, axis=0)
242		age = append(age, zeros(len(globalSeeds), dtype=int32))
243		gste = apply_along_axis(task.eval, 1, globalSeeds)
244		Evaluations = append(Evaluations, gste)
245
246		ib = argmin(Evaluations)
247		age[ib] = 0
248		return Trees, Evaluations, {'age': age}
249

NiaOrg / NiaPy

Pull Request — master (#205)

ForestOptimizationAlgorithm.setParameters() A

Complexity

Size

Duplication

Importance

How to fix Many Parameters

Many Parameters

Duplication Side-by-Side

Filter issues like