NiaPy.task.task.CountingTask.nextIter() - Code Metrics - Inspection of "Upgrade runner" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#206)

by Grega

created 2019-04-25 10:29 UTC

NiaPy.task.task.CountingTask.nextIter() A

↳ Parent: NiaPy.task.task

Complexity

Conditions

Size

Total Lines	8
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	1
dl	0
loc	8
rs	10
c	0
b	0
f	0

# encoding=utf8

"""The implementation of tasks."""

import logging
from enum import Enum

from matplotlib import pyplot as plt, animation as anim
from numpy import inf, random as rand, asarray
from numpy.core.multiarray import ndarray, dot
from numpy.core.umath import fabs

from NiaPy.util import (
    limit_repair,
    fullArray,
    FesException,
    GenException,
    RefException
)
from NiaPy.benchmarks.utility import Utility


logging.basicConfig()
logger = logging.getLogger("NiaPy.task.Task")
logger.setLevel("INFO")


class OptimizationType(Enum):
    r"""Enum representing type of optimization.

    Attributes:
            MINIMIZATION (int): Represents minimization problems and is default optimization type of all algorithms.
            MAXIMIZATION (int): Represents maximization problems.

    """

    MINIMIZATION = 1.0
    MAXIMIZATION = -1.0


class Task:
    r"""Class representing problem to solve with optimization.

    Date:
            2019

    Author:
            Klemen Berkovič

    Attributes:
            D (int): Dimension of the problem.
            Lower (numpy.ndarray): Lower bounds of the problem.
            Upper (numpy.ndarray): Upper bounds of the problem.
            bRange (numpy.ndarray): Search range between upper and lower limits.
            optType (OptimizationType): Optimization type to use.

    See Also:
            * :class:`NiaPy.util.Utility`

    """

    D = 0
    benchmark = None
    Lower, Upper, bRange = inf, inf, inf
    optType = OptimizationType.MINIMIZATION

    def __init__(self, D=0, optType=OptimizationType.MINIMIZATION, benchmark=None, Lower=None, Upper=None, frepair=limit_repair, **kwargs):
        r"""Initialize task class for optimization.

        Arguments:
                D (Optional[int]): Number of dimensions.
                optType (Optional[OptimizationType]): Set the type of optimization.
                benchmark (Union[str, Benchmark]): Problem to solve with optimization.
                Lower (Optional[numpy.ndarray]): Lower limits of the problem.
                Upper (Optional[numpy.ndarray]): Upper limits of the problem.
                frepair (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, Dict[str, Any]], numpy.ndarray]]): Function for reparing individuals components to desired limits.

        See Also:
                * `func`:NiaPy.util.Utility.__init__`
                * `func`:NiaPy.util.Utility.repair`

        """

        # dimension of the problem
        self.D = D
        # set optimization type
        self.optType = optType
        # set optimization function
        self.benchmark = Utility().get_benchmark(benchmark) if benchmark is not None else None

        if self.benchmark is not None:
            self.Fun = self.benchmark.function() if self.benchmark is not None else None

        # set Lower limits
        if Lower is not None:
            self.Lower = fullArray(Lower, self.D)
        elif Lower is None and benchmark is not None:
            self.Lower = fullArray(self.benchmark.Lower, self.D)
        else:
            self.Lower = fullArray(0, self.D)

        # set Upper limits
        if Upper is not None:
            self.Upper = fullArray(Upper, self.D)
        elif Upper is None and benchmark is not None:
            self.Upper = fullArray(self.benchmark.Upper, self.D)
        else:
            self.Upper = fullArray(0, self.D)

        # set range
        self.bRange = self.Upper - self.Lower
        # set repair function
        self.frepair = frepair

    def dim(self):
        r"""Get the number of dimensions.

        Returns:
                int: Dimension of problem optimizing.

        """

        return self.D

    def bcLower(self):
        r"""Get the array of lower bound constraint.

        Returns:
                numpy.ndarray: Lower bound.

        """

        return self.Lower

    def bcUpper(self):
        r"""Get the array of upper bound constraint.

        Returns:
                numpy.ndarray: Upper bound.

        """

        return self.Upper

    def bcRange(self):
        r"""Get the range of bound constraint.

        Returns:
                numpy.ndarray: Range between lower and upper bound.

        """

        return self.Upper - self.Lower

    def repair(self, x, rnd=rand):
        r"""Repair solution and put the solution in the random position inside of the bounds of problem.

        Arguments:
                x (numpy.ndarray): Solution to check and repair if needed.
                rnd (mtrand.RandomState): Random number generator.

        Returns:
                numpy.ndarray: Fixed solution.

        See Also:
                * :func:`NiaPy.util.limitRepair`
                * :func:`NiaPy.util.limitInversRepair`
                * :func:`NiaPy.util.wangRepair`
                * :func:`NiaPy.util.randRepair`
                * :func:`NiaPy.util.reflectRepair`

        """

        return self.frepair(x, self.Lower, self.Upper, rnd=rnd)

    def nextIter(self):
        r"""Increments the number of algorithm iterations."""

    def start(self):
        r"""Start stopwatch."""

    def eval(self, A):
        r"""Evaluate the solution A.

        Arguments:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Fitness/function values of solution.

        """

        return self.Fun(self.D, A) * self.optType.value

    def isFeasible(self, A):
        r"""Check if the solution is feasible.

        Arguments:
                A (Union[numpy.ndarray, Individual]): Solution to check for feasibility.

        Returns:
                bool: `True` if solution is in feasible space else `False`.

        """

        return False not in (A >= self.Lower) and False not in (A <= self.Upper)

    def stopCond(self):
        r"""Check if optimization task should stop.

        Returns:
                bool: `True` if stopping condition is meet else `False`.

        """

        return False


class CountingTask(Task):
    r"""Optimization task with added counting of function evaluations and algorithm iterations/generations.

    Attributes:
            Iters (int): Number of algorithm iterations/generations.
            Evals (int): Number of function evaluations.

    See Also:
            * :class:`NiaPy.util.Task`

    """

    def __init__(self, **kwargs):
        r"""Initialize counting task.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.Task.__init__`

        """

        Task.__init__(self, **kwargs)
        self.Iters, self.Evals = 0, 0

    def eval(self, A):
        r"""Evaluate the solution A.

        This function increments function evaluation counter `self.Evals`.

        Arguments:
                A (numpy.ndarray): Solutions to evaluate.

        Returns:
                float: Fitness/function values of solution.

        See Also:
                * :func:`NiaPy.util.Task.eval`

        """

        r = Task.eval(self, A)
        self.Evals += 1
        return r

    def evals(self):
        r"""Get the number of evaluations made.

        Returns:
                int: Number of evaluations made.

        """

        return self.Evals

    def iters(self):
        r"""Get the number of algorithm iteratins made.

        Returns:
                int: Number of generations/iterations made by algorithm.

        """

        return self.Iters

    def nextIter(self):
        r"""Increases the number of algorithm iterations made.

        This function increments number of algorithm iterations/generations counter `self.Iters`.

        """

        self.Iters += 1


class StoppingTask(CountingTask):
    r"""Optimization task with implemented checking for stopping criterias.

    Attributes:
            nGEN (int): Maximum number of algorithm iterations/generations.
            nFES (int): Maximum number of function evaluations.
            refValue (float): Reference function/fitness values to reach in optimization.
            x (numpy.ndarray): Best found individual.
            x_f (float): Best found individual function/fitness value.

    See Also:
            * :class:`NiaPy.util.CountingTask`

    """

    def __init__(self, nFES=inf, nGEN=inf, refValue=None, **kwargs):
        r"""Initialize task class for optimization.

        Arguments:
                nFES (Optional[int]): Number of function evaluations.
                nGEN (Optional[int]): Number of generations or iterations.
                refValue (Optional[float]): Reference value of function/fitness function.

        See Also:
                * :func:`NiaPy.util.CountingTask.__init__`

        """

        CountingTask.__init__(self, **kwargs)
        self.refValue = (-inf if refValue is None else refValue)
        self.x, self.x_f = None, inf
        self.nFES, self.nGEN = nFES, nGEN

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Fitness/function value of solution.

        See Also:
                * :func:`NiaPy.util.StoppingTask.stopCond`
                * :func:`NiaPy.util.CountingTask.eval`

        """

        if self.stopCond():
            return inf * self.optType.value

        x_f = CountingTask.eval(self, A)

        if x_f < self.x_f:
            self.x_f = x_f

        return x_f

    def stopCond(self):
        r"""Check if stopping condition reached.

        Returns:
                bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`

        """

        return (self.Evals >= self.nFES) or (self.Iters >= self.nGEN) or (self.refValue > self.x_f)

    def stopCondI(self):
        r"""Check if stopping condition reached and increase number of iterations.

        Returns:
                bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.

        See Also:
                * :func:`NiaPy.util.StoppingTask.stopCond`
                * :func:`NiaPy.util.CountingTask.nextIter`

        """

        r = self.stopCond()
        CountingTask.nextIter(self)
        return r


class ThrowingTask(StoppingTask):
    r"""Task that throw exceptions when stopping condition is meet.

    See Also:
            * :class:`NiaPy.util.StoppingTask`

    """

    def __init__(self, **kwargs):
        r"""Initialize optimization task.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)

    def stopCondE(self):
        r"""Throw exception for the given stopping condition.

        Raises:
                * FesException: Thrown when the number of function/fitness evaluations is reached.
                * GenException: Thrown when the number of algorithms generations/iterations is reached.
                * RefException: Thrown when the reference values is reached.
                * TimeException: Thrown when algorithm exceeds time run limit.

        """

        # dtime = datetime.now() - self.startTime
        if self.Evals >= self.nFES:
            raise FesException()
        if self.Iters >= self.nGEN:
            raise GenException()
        # if self.runTime is not None and self.runTime >= dtime: raise TimeException()
        if self.refValue >= self.x_f:
            raise RefException()

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Function/fitness values of solution.

        See Also:
                * :func:`NiaPy.util.ThrowingTask.stopCondE`
                * :func:`NiaPy.util.StoppingTask.eval`

        """

        self.stopCondE()
        return StoppingTask.eval(self, A)


class MoveTask(StoppingTask):
    """Move task implementation."""

    def __init__(self, o=None, fo=None, M=None, fM=None, optF=None, **kwargs):
        r"""Initialize task class for optimization.

        Arguments:
                o (numpy.ndarray[Union[float, int]]): Array for shifting.
                of (Callable[numpy.ndarray[Union[float, int]]]): Function applied on shifted input.
                M (numpy.ndarray[Union[float, int]]): Matrix for rotating.
                fM (Callable[numpy.ndarray[Union[float, int]]]): Function applied after rotating.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)
        self.o = o if isinstance(o, ndarray) or o is None else asarray(o)
        self.M = M if isinstance(M, ndarray) or M is None else asarray(M)
        self.fo, self.fM, self.optF = fo, fM, optF

    def eval(self, A):
        r"""Evaluate the solution.

        Args:
                A (numpy.ndarray): Solution to evaluate

        Returns:
                float: Fitness/function value of solution.

        See Also:
                * :func:`NiaPy.util.StoppingTask.stopCond`
                * :func:`NiaPy.util.StoppingTask.eval`

        """

        if self.stopCond():
            return inf * self.optType.value
        X = A - self.o if self.o is not None else A
        X = self.fo(X) if self.fo is not None else X
        X = dot(X, self.M) if self.M is not None else X
        X = self.fM(X) if self.fM is not None else X
        r = StoppingTask.eval(self, X) + (self.optF if self.optF is not None else 0)
        if r <= self.x_f:
            self.x, self.x_f = A, r
        return r


class ScaledTask(Task):
    r"""Scaled task.

    Attributes:
            _task (Task): Optimization task with evaluation function.
            Lower (numpy.ndarray): Scaled lower limit of search space.
            Upper (numpy.ndarray): Scaled upper limit of search space.

    See Also:
            * :class:`NiaPy.util.Task`

    """

    def __init__(self, task, Lower, Upper, **kwargs):
        r"""Initialize scaled task.

        Args:
                task (Task): Optimization task to scale to new bounds.
                Lower (Union[float, int, numpy.ndarray]): New lower bounds.
                Upper (Union[float, int, numpy.ndarray]): New upper bounds.
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.fullArray`

        """

        Task.__init__(self)
        self._task = task
        self.D = self._task.D
        self.Lower, self.Upper = fullArray(Lower, self.D), fullArray(Upper, self.D)
        self.bRange = fabs(Upper - Lower)

    def stopCond(self):
        r"""Test for stopping condition.

        This function uses `self._task` for checking the stopping criteria.

        Returns:
                bool: `True` if stopping condition is meet else `False`.

        """

        return self._task.stopCond()

    def stopCondI(self):
        r"""Test for stopping condition and increments the number of algorithm generations/iterations.

        This function uses `self._task` for checking the stopping criteria.

        Returns:
                bool: `True` if stopping condition is meet else `False`.

        """

        return self._task.stopCondI()

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution for calculating function/fitness value.

        Returns:
                float: Function values of solution.

        """

        return self._task.eval(A)

    def evals(self):
        r"""Get the number of function evaluations.

        Returns:
                int: Number of function evaluations.

        """

        return self._task.evals()

    def iters(self):
        r"""Get the number of algorithms generations/iterations.

        Returns:
                int: Number of algorithms generations/iterations.

        """

        return self._task.iters()

    def nextIter(self):
        r"""Increment the number of iterations/generations.

        Function uses `self._task` to increment number of generations/iterations.

        """

        self._task.nextIter()


class TaskConvPrint(StoppingTask):
    r"""Task class with printing out new global best solutions found.

    Attributes:
            xb (numpy.ndarray): Global best solution.
            xb_f (float): Global best function/fitness values.

    See Also:
            * :class:`NiaPy.util.StoppingTask`

    """

    def __init__(self, **kwargs):
        r"""Initialize TaskConvPrint class.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)
        self.xb, self.xb_f = None, inf

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (nupy.ndarray): Solution to evaluate.

        Returns:
                float: Function/Fitness values of solution.

        See Also:
                * :func:`NiaPy.util.StoppingTask.eval`

        """

        x_f = StoppingTask.eval(self, A)
        if self.x_f != self.xb_f:
            self.xb, self.xb_f = A, x_f
            logger.info('nFES:%d nGEN:%d => %s -> %s' % (self.Evals, self.Iters, self.xb, self.xb_f * self.optType.value))
        return x_f


class TaskConvSave(StoppingTask):
    r"""Task class with logging of function evaluations need to reach some function vale.

    Attributes:
            evals (List[int]): List of ints representing when the new global best was found.
            x_f_vals (List[float]): List of floats representing function/fitness values found.

    See Also:
            * :class:`NiaPy.util.StoppingTask`

    """

    def __init__(self, **kwargs):
        r"""Initialize TaskConvSave class.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)
        self.evals = []
        self.x_f_vals = []

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Individual/solution to evaluate.

        Returns:
                float: Function/fitness values of individual.

        See Also:
                * :func:`SNiaPy.util.toppingTask.eval`

        """

        x_f = StoppingTask.eval(self, A)
        if x_f <= self.x_f:
            self.evals.append(self.Evals)
            self.x_f_vals.append(x_f)
        return x_f

    def return_conv(self):
        r"""Get values of x and y axis for plotting covariance graph.

        Returns:
                Tuple[List[int], List[float]]:
                        1. List of ints of function evaluations.
                        2. List of ints of function/fitness values.

        """

        return self.evals, self.x_f_vals


class TaskConvPlot(TaskConvSave):
    r"""Task class with ability of showing convergence graph.

    Attributes:
            iters (List[int]): List of ints representing when the new global best was found.
            x_fs (List[float]): List of floats representing function/fitness values found.

    See Also:
            * :class:`NiaPy.util.StoppingTask`

    """

    def __init__(self, **kwargs):
        r"""TODO.

        Args:
                **kwargs (Dict[str, Any]): Additional arguments.

        See Also:
                * :func:`NiaPy.util.StoppingTask.__init__`

        """

        StoppingTask.__init__(self, **kwargs)
        self.fig = plt.figure()
        self.ax = self.fig.subplots(nrows=1, ncols=1)
        self.ax.set_xlim(0, self.nFES)
        self.line, = self.ax.plot(self.iters, self.x_fs, animated=True)
        self.ani = anim.FuncAnimation(self.fig, self.updatePlot, blit=True)
        self.showPlot()

    def eval(self, A):
        r"""Evaluate solution.

        Args:
                A (numpy.ndarray): Solution to evaluate.

        Returns:
                float: Fitness/function values of solution.

        """

        x_f = StoppingTask.eval(self, A)
        if not self.x_f_vals:
            self.x_f_vals.append(x_f)
        elif x_f < self.x_f_vals[-1]:
            self.x_f_vals.append(x_f)
        else:
            self.x_f_vals.append(self.x_f_vals[-1])
        self.evals.append(self.Evals)
        return x_f

    def showPlot(self):
        r"""Animation updating function."""
        plt.show(block=False)
        plt.pause(0.001)

    def updatePlot(self, frame):
        r"""Update mathplotlib figure.

        Args:
                frame (): TODO

        Returns:
                Tuple[List[float], Any]:
                        1. Line

        """

        if self.x_f_vals:
            max_fs, min_fs = self.x_f_vals[0], self.x_f_vals[-1]
            self.ax.set_ylim(min_fs + 1, max_fs + 1)
            self.line.set_data(self.evals, self.x_f_vals)
        return self.line,


class TaskComposition(MoveTask):
    """Task compostion."""

    def __init__(self, benchmarks=None, rho=None, lamb=None, bias=None, **kwargs):
        r"""Initialize of composite function problem.

        Arguments:
                benchmarks (List[Benchmark]): Optimization function to use in composition
                delta (numpy.ndarray[float]): TODO
                lamb (numpy.ndarray[float]): TODO
                bias (numpy.ndarray[float]): TODO

        See Also:
                * :func:`NiaPy.util.MoveTask.__init__`

        TODO:
                Class is a work in progress.

        """

        MoveTask.__init__(self, **kwargs)

    def eval(self, A):
        r"""TODO.

        Args:
                A:

        Returns:
                float:

        Todo:
                Usage of multiple functions on the same time

        """

        return inf


1			# encoding=utf8
2
3			"""The implementation of tasks."""
4
5			import logging
6			from enum import Enum
7
8			from matplotlib import pyplot as plt, animation as anim
9			from numpy import inf, random as rand, asarray
10			from numpy.core.multiarray import ndarray, dot
11			from numpy.core.umath import fabs
12
13			from NiaPy.util import (
14			limit_repair,
15			fullArray,
16			FesException,
17			GenException,
18			RefException
19			)
20			from NiaPy.benchmarks.utility import Utility
21
22
23			logging.basicConfig()
24			logger = logging.getLogger("NiaPy.task.Task")
25			logger.setLevel("INFO")
26
27
28			class OptimizationType(Enum):
29			r"""Enum representing type of optimization.
30
31			Attributes:
32			MINIMIZATION (int): Represents minimization problems and is default optimization type of all algorithms.
33			MAXIMIZATION (int): Represents maximization problems.
34
35			"""
36
37			MINIMIZATION = 1.0
38			MAXIMIZATION = -1.0
39
40
41			class Task:
42			r"""Class representing problem to solve with optimization.
43
44			Date:
45			2019
46
47			Author:
48			Klemen Berkovič
49
50			Attributes:
51			D (int): Dimension of the problem.
52			Lower (numpy.ndarray): Lower bounds of the problem.
53			Upper (numpy.ndarray): Upper bounds of the problem.
54			bRange (numpy.ndarray): Search range between upper and lower limits.
55			optType (OptimizationType): Optimization type to use.
56
57			See Also:
58			* :class:`NiaPy.util.Utility`
59
60			"""
61
62			D = 0
63			benchmark = None
64			Lower, Upper, bRange = inf, inf, inf
65			optType = OptimizationType.MINIMIZATION
66
67			def __init__(self, D=0, optType=OptimizationType.MINIMIZATION, benchmark=None, Lower=None, Upper=None, frepair=limit_repair, **kwargs):
68			r"""Initialize task class for optimization.
69
70			Arguments:
71			D (Optional[int]): Number of dimensions.
72			optType (Optional[OptimizationType]): Set the type of optimization.
73			benchmark (Union[str, Benchmark]): Problem to solve with optimization.
74			Lower (Optional[numpy.ndarray]): Lower limits of the problem.
75			Upper (Optional[numpy.ndarray]): Upper limits of the problem.
76			frepair (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, Dict[str, Any]], numpy.ndarray]]): Function for reparing individuals components to desired limits.
77
78			See Also:
79			* `func`:NiaPy.util.Utility.__init__`
80			* `func`:NiaPy.util.Utility.repair`
81
82			"""
83
84			# dimension of the problem
85			self.D = D
86			# set optimization type
87			self.optType = optType
88			# set optimization function
89			self.benchmark = Utility().get_benchmark(benchmark) if benchmark is not None else None
90
91			if self.benchmark is not None:
92			self.Fun = self.benchmark.function() if self.benchmark is not None else None
93
94			# set Lower limits
95			if Lower is not None:
96			self.Lower = fullArray(Lower, self.D)
97			elif Lower is None and benchmark is not None:
98			self.Lower = fullArray(self.benchmark.Lower, self.D)
99			else:
100			self.Lower = fullArray(0, self.D)
101
102			# set Upper limits
103			if Upper is not None:
104			self.Upper = fullArray(Upper, self.D)
105			elif Upper is None and benchmark is not None:
106			self.Upper = fullArray(self.benchmark.Upper, self.D)
107			else:
108			self.Upper = fullArray(0, self.D)
109
110			# set range
111			self.bRange = self.Upper - self.Lower
112			# set repair function
113			self.frepair = frepair
114
115			def dim(self):
116			r"""Get the number of dimensions.
117
118			Returns:
119			int: Dimension of problem optimizing.
120
121			"""
122
123			return self.D
124
125			def bcLower(self):
126			r"""Get the array of lower bound constraint.
127
128			Returns:
129			numpy.ndarray: Lower bound.
130
131			"""
132
133			return self.Lower
134
135			def bcUpper(self):
136			r"""Get the array of upper bound constraint.
137
138			Returns:
139			numpy.ndarray: Upper bound.
140
141			"""
142
143			return self.Upper
144
145			def bcRange(self):
146			r"""Get the range of bound constraint.
147
148			Returns:
149			numpy.ndarray: Range between lower and upper bound.
150
151			"""
152
153			return self.Upper - self.Lower
154
155			def repair(self, x, rnd=rand):
156			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
157
158			Arguments:
159			x (numpy.ndarray): Solution to check and repair if needed.
160			rnd (mtrand.RandomState): Random number generator.
161
162			Returns:
163			numpy.ndarray: Fixed solution.
164
165			See Also:
166			* :func:`NiaPy.util.limitRepair`
167			* :func:`NiaPy.util.limitInversRepair`
168			* :func:`NiaPy.util.wangRepair`
169			* :func:`NiaPy.util.randRepair`
170			* :func:`NiaPy.util.reflectRepair`
171
172			"""
173
174			return self.frepair(x, self.Lower, self.Upper, rnd=rnd)
175
176			def nextIter(self):
177			r"""Increments the number of algorithm iterations."""
178
179			def start(self):
180			r"""Start stopwatch."""
181
182			def eval(self, A):
183			r"""Evaluate the solution A.
184
185			Arguments:
186			A (numpy.ndarray): Solution to evaluate.
187
188			Returns:
189			float: Fitness/function values of solution.
190
191			"""
192
193			return self.Fun(self.D, A) * self.optType.value
194
195			def isFeasible(self, A):
196			r"""Check if the solution is feasible.
197
198			Arguments:
199			A (Union[numpy.ndarray, Individual]): Solution to check for feasibility.
200
201			Returns:
202			bool: `True` if solution is in feasible space else `False`.
203
204			"""
205
206			return False not in (A >= self.Lower) and False not in (A <= self.Upper)
207
208			def stopCond(self):
209			r"""Check if optimization task should stop.
210
211			Returns:
212			bool: `True` if stopping condition is meet else `False`.
213
214			"""
215
216			return False
217
218
219			class CountingTask(Task):
220			r"""Optimization task with added counting of function evaluations and algorithm iterations/generations.
221
222			Attributes:
223			Iters (int): Number of algorithm iterations/generations.
224			Evals (int): Number of function evaluations.
225
226			See Also:
227			* :class:`NiaPy.util.Task`
228
229			"""
230
231			def __init__(self, **kwargs):
232			r"""Initialize counting task.
233
234			Args:
235			**kwargs (Dict[str, Any]): Additional arguments.
236
237			See Also:
238			* :func:`NiaPy.util.Task.__init__`
239
240			"""
241
242			Task.__init__(self, **kwargs)
243			self.Iters, self.Evals = 0, 0
244
245			def eval(self, A):
246			r"""Evaluate the solution A.
247
248			This function increments function evaluation counter `self.Evals`.
249
250			Arguments:
251			A (numpy.ndarray): Solutions to evaluate.
252
253			Returns:
254			float: Fitness/function values of solution.
255
256			See Also:
257			* :func:`NiaPy.util.Task.eval`
258
259			"""
260
261			r = Task.eval(self, A)
262			self.Evals += 1
263			return r
264
265			def evals(self):
266			r"""Get the number of evaluations made.
267
268			Returns:
269			int: Number of evaluations made.
270
271			"""
272
273			return self.Evals
274
275			def iters(self):
276			r"""Get the number of algorithm iteratins made.
277
278			Returns:
279			int: Number of generations/iterations made by algorithm.
280
281			"""
282
283			return self.Iters
284
285			def nextIter(self):
286			r"""Increases the number of algorithm iterations made.
287
288			This function increments number of algorithm iterations/generations counter `self.Iters`.
289
290			"""
291
292			self.Iters += 1
293
294
295			class StoppingTask(CountingTask):
296			r"""Optimization task with implemented checking for stopping criterias.
297
298			Attributes:
299			nGEN (int): Maximum number of algorithm iterations/generations.
300			nFES (int): Maximum number of function evaluations.
301			refValue (float): Reference function/fitness values to reach in optimization.
302			x (numpy.ndarray): Best found individual.
303			x_f (float): Best found individual function/fitness value.
304
305			See Also:
306			* :class:`NiaPy.util.CountingTask`
307
308			"""
309
310			def __init__(self, nFES=inf, nGEN=inf, refValue=None, **kwargs):
311			r"""Initialize task class for optimization.
312
313			Arguments:
314			nFES (Optional[int]): Number of function evaluations.
315			nGEN (Optional[int]): Number of generations or iterations.
316			refValue (Optional[float]): Reference value of function/fitness function.
317
318			See Also:
319			* :func:`NiaPy.util.CountingTask.__init__`
320
321			"""
322
323			CountingTask.__init__(self, **kwargs)
324			self.refValue = (-inf if refValue is None else refValue)
325			self.x, self.x_f = None, inf
326			self.nFES, self.nGEN = nFES, nGEN
327
328			def eval(self, A):
329			r"""Evaluate solution.
330
331			Args:
332			A (numpy.ndarray): Solution to evaluate.
333
334			Returns:
335			float: Fitness/function value of solution.
336
337			See Also:
338			* :func:`NiaPy.util.StoppingTask.stopCond`
339			* :func:`NiaPy.util.CountingTask.eval`
340
341			"""
342
343			if self.stopCond():
344			return inf * self.optType.value
345
346			x_f = CountingTask.eval(self, A)
347
348			if x_f < self.x_f:
349			self.x_f = x_f
350
351			return x_f
352
353			def stopCond(self):
354			r"""Check if stopping condition reached.
355
356			Returns:
357			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`
358
359			"""
360
361			return (self.Evals >= self.nFES) or (self.Iters >= self.nGEN) or (self.refValue > self.x_f)
362
363			def stopCondI(self):
364			r"""Check if stopping condition reached and increase number of iterations.
365
366			Returns:
367			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.
368
369			See Also:
370			* :func:`NiaPy.util.StoppingTask.stopCond`
371			* :func:`NiaPy.util.CountingTask.nextIter`
372
373			"""
374
375			r = self.stopCond()
376			CountingTask.nextIter(self)
377			return r
378
379
380			class ThrowingTask(StoppingTask):
381			r"""Task that throw exceptions when stopping condition is meet.
382
383			See Also:
384			* :class:`NiaPy.util.StoppingTask`
385
386			"""
387
388			def __init__(self, **kwargs):
389			r"""Initialize optimization task.
390
391			Args:
392			**kwargs (Dict[str, Any]): Additional arguments.
393
394			See Also:
395			* :func:`NiaPy.util.StoppingTask.__init__`
396
397			"""
398
399			StoppingTask.__init__(self, **kwargs)
400
401			def stopCondE(self):
402			r"""Throw exception for the given stopping condition.
403
404			Raises:
405			* FesException: Thrown when the number of function/fitness evaluations is reached.
406			* GenException: Thrown when the number of algorithms generations/iterations is reached.
407			* RefException: Thrown when the reference values is reached.
408			* TimeException: Thrown when algorithm exceeds time run limit.
409
410			"""
411
412			# dtime = datetime.now() - self.startTime
413			if self.Evals >= self.nFES:
414			raise FesException()
415			if self.Iters >= self.nGEN:
416			raise GenException()
417			# if self.runTime is not None and self.runTime >= dtime: raise TimeException()
418			if self.refValue >= self.x_f:
419			raise RefException()
420
421			def eval(self, A):
422			r"""Evaluate solution.
423
424			Args:
425			A (numpy.ndarray): Solution to evaluate.
426
427			Returns:
428			float: Function/fitness values of solution.
429
430			See Also:
431			* :func:`NiaPy.util.ThrowingTask.stopCondE`
432			* :func:`NiaPy.util.StoppingTask.eval`
433
434			"""
435
436			self.stopCondE()
437			return StoppingTask.eval(self, A)
438
439
440			class MoveTask(StoppingTask):
441			"""Move task implementation."""
442
443			def __init__(self, o=None, fo=None, M=None, fM=None, optF=None, **kwargs):
444			r"""Initialize task class for optimization.
445
446			Arguments:
447			o (numpy.ndarray[Union[float, int]]): Array for shifting.
448			of (Callable[numpy.ndarray[Union[float, int]]]): Function applied on shifted input.
449			M (numpy.ndarray[Union[float, int]]): Matrix for rotating.
450			fM (Callable[numpy.ndarray[Union[float, int]]]): Function applied after rotating.
451
452			See Also:
453			* :func:`NiaPy.util.StoppingTask.__init__`
454
455			"""
456
457			StoppingTask.__init__(self, **kwargs)
458			self.o = o if isinstance(o, ndarray) or o is None else asarray(o)
459			self.M = M if isinstance(M, ndarray) or M is None else asarray(M)
460			self.fo, self.fM, self.optF = fo, fM, optF
461
462			def eval(self, A):
463			r"""Evaluate the solution.
464
465			Args:
466			A (numpy.ndarray): Solution to evaluate
467
468			Returns:
469			float: Fitness/function value of solution.
470
471			See Also:
472			* :func:`NiaPy.util.StoppingTask.stopCond`
473			* :func:`NiaPy.util.StoppingTask.eval`
474
475			"""
476
477			if self.stopCond():
478			return inf * self.optType.value
479			X = A - self.o if self.o is not None else A
480			X = self.fo(X) if self.fo is not None else X
481			X = dot(X, self.M) if self.M is not None else X
482			X = self.fM(X) if self.fM is not None else X
483			r = StoppingTask.eval(self, X) + (self.optF if self.optF is not None else 0)
484			if r <= self.x_f:
485			self.x, self.x_f = A, r
486			return r
487
488
489			class ScaledTask(Task):
490			r"""Scaled task.
491
492			Attributes:
493			_task (Task): Optimization task with evaluation function.
494			Lower (numpy.ndarray): Scaled lower limit of search space.
495			Upper (numpy.ndarray): Scaled upper limit of search space.
496
497			See Also:
498			* :class:`NiaPy.util.Task`
499
500			"""
501
502			def __init__(self, task, Lower, Upper, **kwargs):
503			r"""Initialize scaled task.
504
505			Args:
506			task (Task): Optimization task to scale to new bounds.
507			Lower (Union[float, int, numpy.ndarray]): New lower bounds.
508			Upper (Union[float, int, numpy.ndarray]): New upper bounds.
509			**kwargs (Dict[str, Any]): Additional arguments.
510
511			See Also:
512			* :func:`NiaPy.util.fullArray`
513
514			"""
515
516			Task.__init__(self)
517			self._task = task
518			self.D = self._task.D
519			self.Lower, self.Upper = fullArray(Lower, self.D), fullArray(Upper, self.D)
520			self.bRange = fabs(Upper - Lower)
521
522			def stopCond(self):
523			r"""Test for stopping condition.
524
525			This function uses `self._task` for checking the stopping criteria.
526
527			Returns:
528			bool: `True` if stopping condition is meet else `False`.
529
530			"""
531
532			return self._task.stopCond()
533
534			def stopCondI(self):
535			r"""Test for stopping condition and increments the number of algorithm generations/iterations.
536
537			This function uses `self._task` for checking the stopping criteria.
538
539			Returns:
540			bool: `True` if stopping condition is meet else `False`.
541
542			"""
543
544			return self._task.stopCondI()
545
546			def eval(self, A):
547			r"""Evaluate solution.
548
549			Args:
550			A (numpy.ndarray): Solution for calculating function/fitness value.
551
552			Returns:
553			float: Function values of solution.
554
555			"""
556
557			return self._task.eval(A)
558
559			def evals(self):
560			r"""Get the number of function evaluations.
561
562			Returns:
563			int: Number of function evaluations.
564
565			"""
566
567			return self._task.evals()
568
569			def iters(self):
570			r"""Get the number of algorithms generations/iterations.
571
572			Returns:
573			int: Number of algorithms generations/iterations.
574
575			"""
576
577			return self._task.iters()
578
579			def nextIter(self):
580			r"""Increment the number of iterations/generations.
581
582			Function uses `self._task` to increment number of generations/iterations.
583
584			"""
585
586			self._task.nextIter()
587
588
589			class TaskConvPrint(StoppingTask):
590			r"""Task class with printing out new global best solutions found.
591
592			Attributes:
593			xb (numpy.ndarray): Global best solution.
594			xb_f (float): Global best function/fitness values.
595
596			See Also:
597			* :class:`NiaPy.util.StoppingTask`
598
599			"""
600
601			def __init__(self, **kwargs):
602			r"""Initialize TaskConvPrint class.
603
604			Args:
605			**kwargs (Dict[str, Any]): Additional arguments.
606
607			See Also:
608			* :func:`NiaPy.util.StoppingTask.__init__`
609
610			"""
611
612			StoppingTask.__init__(self, **kwargs)
613			self.xb, self.xb_f = None, inf
614
615			def eval(self, A):
616			r"""Evaluate solution.
617
618			Args:
619			A (nupy.ndarray): Solution to evaluate.
620
621			Returns:
622			float: Function/Fitness values of solution.
623
624			See Also:
625			* :func:`NiaPy.util.StoppingTask.eval`
626
627			"""
628
629			x_f = StoppingTask.eval(self, A)
630			if self.x_f != self.xb_f:
631			self.xb, self.xb_f = A, x_f
632			logger.info('nFES:%d nGEN:%d => %s -> %s' % (self.Evals, self.Iters, self.xb, self.xb_f * self.optType.value))
633			return x_f
634
635
636			class TaskConvSave(StoppingTask):
637			r"""Task class with logging of function evaluations need to reach some function vale.
638
639			Attributes:
640			evals (List[int]): List of ints representing when the new global best was found.
641			x_f_vals (List[float]): List of floats representing function/fitness values found.
642
643			See Also:
644			* :class:`NiaPy.util.StoppingTask`
645
646			"""
647
648			def __init__(self, **kwargs):
649			r"""Initialize TaskConvSave class.
650
651			Args:
652			**kwargs (Dict[str, Any]): Additional arguments.
653
654			See Also:
655			* :func:`NiaPy.util.StoppingTask.__init__`
656
657			"""
658
659			StoppingTask.__init__(self, **kwargs)
660			self.evals = []
661			self.x_f_vals = []
662
663			def eval(self, A):
664			r"""Evaluate solution.
665
666			Args:
667			A (numpy.ndarray): Individual/solution to evaluate.
668
669			Returns:
670			float: Function/fitness values of individual.
671
672			See Also:
673			* :func:`SNiaPy.util.toppingTask.eval`
674
675			"""
676
677			x_f = StoppingTask.eval(self, A)
678			if x_f <= self.x_f:
679			self.evals.append(self.Evals)
680			self.x_f_vals.append(x_f)
681			return x_f
682
683			def return_conv(self):
684			r"""Get values of x and y axis for plotting covariance graph.
685
686			Returns:
687			Tuple[List[int], List[float]]:
688			1. List of ints of function evaluations.
689			2. List of ints of function/fitness values.
690
691			"""
692
693			return self.evals, self.x_f_vals
694
695
696			class TaskConvPlot(TaskConvSave):
697			r"""Task class with ability of showing convergence graph.
698
699			Attributes:
700			iters (List[int]): List of ints representing when the new global best was found.
701			x_fs (List[float]): List of floats representing function/fitness values found.
702
703			See Also:
704			* :class:`NiaPy.util.StoppingTask`
705
706			"""
707
708			def __init__(self, **kwargs):
709			r"""TODO.
710
711			Args:
712			**kwargs (Dict[str, Any]): Additional arguments.
713
714			See Also:
715			* :func:`NiaPy.util.StoppingTask.__init__`
716
717			"""
718
719			StoppingTask.__init__(self, **kwargs)
720			self.fig = plt.figure()
721			self.ax = self.fig.subplots(nrows=1, ncols=1)
722			self.ax.set_xlim(0, self.nFES)
723			self.line, = self.ax.plot(self.iters, self.x_fs, animated=True)
724			self.ani = anim.FuncAnimation(self.fig, self.updatePlot, blit=True)
725			self.showPlot()
726
727			def eval(self, A):
728			r"""Evaluate solution.
729
730			Args:
731			A (numpy.ndarray): Solution to evaluate.
732
733			Returns:
734			float: Fitness/function values of solution.
735
736			"""
737
738			x_f = StoppingTask.eval(self, A)
739			if not self.x_f_vals:
740			self.x_f_vals.append(x_f)
741			elif x_f < self.x_f_vals[-1]:
742			self.x_f_vals.append(x_f)
743			else:
744			self.x_f_vals.append(self.x_f_vals[-1])
745			self.evals.append(self.Evals)
746			return x_f
747
748			def showPlot(self):
749			r"""Animation updating function."""
750			plt.show(block=False)
751			plt.pause(0.001)
752
753			def updatePlot(self, frame):
754			r"""Update mathplotlib figure.
755
756			Args:
757			frame (): TODO
758
759			Returns:
760			Tuple[List[float], Any]:
761			1. Line
762
763			"""
764
765			if self.x_f_vals:
766			max_fs, min_fs = self.x_f_vals[0], self.x_f_vals[-1]
767			self.ax.set_ylim(min_fs + 1, max_fs + 1)
768			self.line.set_data(self.evals, self.x_f_vals)
769			return self.line,
770
771
772			class TaskComposition(MoveTask):
773			"""Task compostion."""
774
775			def __init__(self, benchmarks=None, rho=None, lamb=None, bias=None, **kwargs):
776			r"""Initialize of composite function problem.
777
778			Arguments:
779			benchmarks (List[Benchmark]): Optimization function to use in composition
780			delta (numpy.ndarray[float]): TODO
781			lamb (numpy.ndarray[float]): TODO
782			bias (numpy.ndarray[float]): TODO
783
784			See Also:
785			* :func:`NiaPy.util.MoveTask.__init__`
786
787			TODO:
788			Class is a work in progress.
789
790			"""
791
792			MoveTask.__init__(self, **kwargs)
793
794			def eval(self, A):
795			r"""TODO.
796
797			Args:
798			A:
799
800			Returns:
801			float:
802
803			Todo:
804			Usage of multiple functions on the same time
805
806			"""
807
808			return inf
809

NiaOrg / NiaPy

Pull Request — master (#206)

NiaPy.task.task.CountingTask.nextIter() A

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