NiaPy.task.task.Task.__init__() - Code Metrics - Inspection of "Removing ScalingTask & MoveTask" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#209)

unknown

created 2019-04-26 10:19 UTC

NiaPy.task.task.Task.init() C

↳ Parent: NiaPy.task.task

Complexity

Conditions

Size

Total Lines	47
Code Lines	18

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	10
eloc	18
nop	8
dl	0
loc	47
rs	5.9999
c	0
b	0
f	0

How to fix Complexity Many Parameters

# 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

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 and others

    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, logger=False, **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.

        Note:
                Storing improvements during the evolutionary cycle is
                captured in self.n_evals and self.x_f_vals

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

        """

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

    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
            self.n_evals.append(self.Evals)
            self.x_f_vals.append(x_f)
            if self.logger:
                logger.info('nFES:%d => %s' % (self.Evals, self.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

    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 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 TaskConvPlot():
    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,


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
10
11			from NiaPy.util import (
12			limit_repair,
13			fullArray,
14			FesException,
15			GenException,
16			RefException
17			)
18			from NiaPy.benchmarks.utility import Utility
19
20
21			logging.basicConfig()
22			logger = logging.getLogger("NiaPy.task.Task")
23			logger.setLevel("INFO")
24
25
26			class OptimizationType(Enum):
27			r"""Enum representing type of optimization.
28
29			Attributes:
30			MINIMIZATION (int): Represents minimization problems and is default optimization type of all algorithms.
31			MAXIMIZATION (int): Represents maximization problems.
32
33			"""
34
35			MINIMIZATION = 1.0
36			MAXIMIZATION = -1.0
37
38
39			class Task:
40			r"""Class representing problem to solve with optimization.
41
42			Date:
43			2019
44
45			Author:
46			Klemen Berkovič and others
47
48			Attributes:
49			D (int): Dimension of the problem.
50			Lower (numpy.ndarray): Lower bounds of the problem.
51			Upper (numpy.ndarray): Upper bounds of the problem.
52			bRange (numpy.ndarray): Search range between upper and lower limits.
53			optType (OptimizationType): Optimization type to use.
54
55			See Also:
56			* :class:`NiaPy.util.Utility`
57
58			"""
59
60			D = 0
61			benchmark = None
62			Lower, Upper, bRange = inf, inf, inf
63			optType = OptimizationType.MINIMIZATION
64
65			def __init__(self, D=0, optType=OptimizationType.MINIMIZATION, benchmark=None, Lower=None, Upper=None, frepair=limit_repair, **kwargs):
66			r"""Initialize task class for optimization.
67
68			Arguments:
69			D (Optional[int]): Number of dimensions.
70			optType (Optional[OptimizationType]): Set the type of optimization.
71			benchmark (Union[str, Benchmark]): Problem to solve with optimization.
72			Lower (Optional[numpy.ndarray]): Lower limits of the problem.
73			Upper (Optional[numpy.ndarray]): Upper limits of the problem.
74			frepair (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, Dict[str, Any]], numpy.ndarray]]): Function for reparing individuals components to desired limits.
75
76			See Also:
77			* `func`:NiaPy.util.Utility.__init__`
78			* `func`:NiaPy.util.Utility.repair`
79
80			"""
81
82			# dimension of the problem
83			self.D = D
84			# set optimization type
85			self.optType = optType
86			# set optimization function
87			self.benchmark = Utility().get_benchmark(benchmark) if benchmark is not None else None
88
89			if self.benchmark is not None:
90			self.Fun = self.benchmark.function() if self.benchmark is not None else None
91
92			# set Lower limits
93			if Lower is not None:
94			self.Lower = fullArray(Lower, self.D)
95			elif Lower is None and benchmark is not None:
96			self.Lower = fullArray(self.benchmark.Lower, self.D)
97			else:
98			self.Lower = fullArray(0, self.D)
99
100			# set Upper limits
101			if Upper is not None:
102			self.Upper = fullArray(Upper, self.D)
103			elif Upper is None and benchmark is not None:
104			self.Upper = fullArray(self.benchmark.Upper, self.D)
105			else:
106			self.Upper = fullArray(0, self.D)
107
108			# set range
109			self.bRange = self.Upper - self.Lower
110			# set repair function
111			self.frepair = frepair
112
113			def dim(self):
114			r"""Get the number of dimensions.
115
116			Returns:
117			int: Dimension of problem optimizing.
118
119			"""
120
121			return self.D
122
123			def bcLower(self):
124			r"""Get the array of lower bound constraint.
125
126			Returns:
127			numpy.ndarray: Lower bound.
128
129			"""
130
131			return self.Lower
132
133			def bcUpper(self):
134			r"""Get the array of upper bound constraint.
135
136			Returns:
137			numpy.ndarray: Upper bound.
138
139			"""
140
141			return self.Upper
142
143			def bcRange(self):
144			r"""Get the range of bound constraint.
145
146			Returns:
147			numpy.ndarray: Range between lower and upper bound.
148
149			"""
150
151			return self.Upper - self.Lower
152
153			def repair(self, x, rnd=rand):
154			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
155
156			Arguments:
157			x (numpy.ndarray): Solution to check and repair if needed.
158			rnd (mtrand.RandomState): Random number generator.
159
160			Returns:
161			numpy.ndarray: Fixed solution.
162
163			See Also:
164			* :func:`NiaPy.util.limitRepair`
165			* :func:`NiaPy.util.limitInversRepair`
166			* :func:`NiaPy.util.wangRepair`
167			* :func:`NiaPy.util.randRepair`
168			* :func:`NiaPy.util.reflectRepair`
169
170			"""
171
172			return self.frepair(x, self.Lower, self.Upper, rnd=rnd)
173
174			def nextIter(self):
175			r"""Increments the number of algorithm iterations."""
176
177			def start(self):
178			r"""Start stopwatch."""
179
180			def eval(self, A):
181			r"""Evaluate the solution A.
182
183			Arguments:
184			A (numpy.ndarray): Solution to evaluate.
185
186			Returns:
187			float: Fitness/function values of solution.
188
189			"""
190
191			return self.Fun(self.D, A) * self.optType.value
192
193			def isFeasible(self, A):
194			r"""Check if the solution is feasible.
195
196			Arguments:
197			A (Union[numpy.ndarray, Individual]): Solution to check for feasibility.
198
199			Returns:
200			bool: `True` if solution is in feasible space else `False`.
201
202			"""
203
204			return False not in (A >= self.Lower) and False not in (A <= self.Upper)
205
206			def stopCond(self):
207			r"""Check if optimization task should stop.
208
209			Returns:
210			bool: `True` if stopping condition is meet else `False`.
211
212			"""
213
214			return False
215
216
217			class CountingTask(Task):
218			r"""Optimization task with added counting of function evaluations and algorithm iterations/generations.
219
220			Attributes:
221			Iters (int): Number of algorithm iterations/generations.
222			Evals (int): Number of function evaluations.
223
224			See Also:
225			* :class:`NiaPy.util.Task`
226
227			"""
228
229			def __init__(self, **kwargs):
230			r"""Initialize counting task.
231
232			Args:
233			**kwargs (Dict[str, Any]): Additional arguments.
234
235			See Also:
236			* :func:`NiaPy.util.Task.__init__`
237
238			"""
239
240			Task.__init__(self, **kwargs)
241			self.Iters, self.Evals = 0, 0
242
243			def eval(self, A):
244			r"""Evaluate the solution A.
245
246			This function increments function evaluation counter `self.Evals`.
247
248			Arguments:
249			A (numpy.ndarray): Solutions to evaluate.
250
251			Returns:
252			float: Fitness/function values of solution.
253
254			See Also:
255			* :func:`NiaPy.util.Task.eval`
256
257			"""
258
259			r = Task.eval(self, A)
260			self.Evals += 1
261			return r
262
263			def evals(self):
264			r"""Get the number of evaluations made.
265
266			Returns:
267			int: Number of evaluations made.
268
269			"""
270
271			return self.Evals
272
273			def iters(self):
274			r"""Get the number of algorithm iteratins made.
275
276			Returns:
277			int: Number of generations/iterations made by algorithm.
278
279			"""
280
281			return self.Iters
282
283			def nextIter(self):
284			r"""Increases the number of algorithm iterations made.
285
286			This function increments number of algorithm iterations/generations counter `self.Iters`.
287
288			"""
289
290			self.Iters += 1
291
292
293			class StoppingTask(CountingTask):
294			r"""Optimization task with implemented checking for stopping criterias.
295
296			Attributes:
297			nGEN (int): Maximum number of algorithm iterations/generations.
298			nFES (int): Maximum number of function evaluations.
299			refValue (float): Reference function/fitness values to reach in optimization.
300			x (numpy.ndarray): Best found individual.
301			x_f (float): Best found individual function/fitness value.
302
303			See Also:
304			* :class:`NiaPy.util.CountingTask`
305
306			"""
307
308			def __init__(self, nFES=inf, nGEN=inf, refValue=None, logger=False, **kwargs):
309			r"""Initialize task class for optimization.
310
311			Arguments:
312			nFES (Optional[int]): Number of function evaluations.
313			nGEN (Optional[int]): Number of generations or iterations.
314			refValue (Optional[float]): Reference value of function/fitness function.
315
316			Note:
317			Storing improvements during the evolutionary cycle is
318			captured in self.n_evals and self.x_f_vals
319
320			See Also:
321			* :func:`NiaPy.util.CountingTask.__init__`
322
323			"""
324
325			CountingTask.__init__(self, **kwargs)
326			self.refValue = (-inf if refValue is None else refValue)
327			self.logger = logger
328			self.x, self.x_f = None, inf
329			self.nFES, self.nGEN = nFES, nGEN
330			self.n_evals = []
331			self.x_f_vals = []
332
333			def eval(self, A):
334			r"""Evaluate solution.
335
336			Args:
337			A (numpy.ndarray): Solution to evaluate.
338
339			Returns:
340			float: Fitness/function value of solution.
341
342			See Also:
343			* :func:`NiaPy.util.StoppingTask.stopCond`
344			* :func:`NiaPy.util.CountingTask.eval`
345
346			"""
347
348			if self.stopCond():
349			return inf * self.optType.value
350
351			x_f = CountingTask.eval(self, A)
352
353			if x_f < self.x_f:
354			self.x_f = x_f
355			self.n_evals.append(self.Evals)
356			self.x_f_vals.append(x_f)
357			if self.logger:
358			logger.info('nFES:%d => %s' % (self.Evals, self.x_f))
359
360			return x_f
361
362			def stopCond(self):
363			r"""Check if stopping condition reached.
364
365			Returns:
366			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`
367
368			"""
369
370			return (self.Evals >= self.nFES) or (self.Iters >= self.nGEN) or (self.refValue > self.x_f)
371
372			def stopCondI(self):
373			r"""Check if stopping condition reached and increase number of iterations.
374
375			Returns:
376			bool: `True` if number of function evaluations or number of algorithm iterations/generations or reference values is reach else `False`.
377
378			See Also:
379			* :func:`NiaPy.util.StoppingTask.stopCond`
380			* :func:`NiaPy.util.CountingTask.nextIter`
381
382			"""
383
384			r = self.stopCond()
385			CountingTask.nextIter(self)
386			return r
387
388			def return_conv(self):
389			r"""Get values of x and y axis for plotting covariance graph.
390
391			Returns:
392			Tuple[List[int], List[float]]:
393			1. List of ints of function evaluations.
394			2. List of ints of function/fitness values.
395
396			"""
397			return self.evals, self.x_f_vals
398
399
400			class ThrowingTask(StoppingTask):
401			r"""Task that throw exceptions when stopping condition is meet.
402
403			See Also:
404			* :class:`NiaPy.util.StoppingTask`
405
406			"""
407
408			def __init__(self, **kwargs):
409			r"""Initialize optimization task.
410
411			Args:
412			**kwargs (Dict[str, Any]): Additional arguments.
413
414			See Also:
415			* :func:`NiaPy.util.StoppingTask.__init__`
416
417			"""
418
419			StoppingTask.__init__(self, **kwargs)
420
421			def stopCondE(self):
422			r"""Throw exception for the given stopping condition.
423
424			Raises:
425			* FesException: Thrown when the number of function/fitness evaluations is reached.
426			* GenException: Thrown when the number of algorithms generations/iterations is reached.
427			* RefException: Thrown when the reference values is reached.
428			* TimeException: Thrown when algorithm exceeds time run limit.
429
430			"""
431
432			# dtime = datetime.now() - self.startTime
433			if self.Evals >= self.nFES:
434			raise FesException()
435			if self.Iters >= self.nGEN:
436			raise GenException()
437			# if self.runTime is not None and self.runTime >= dtime: raise TimeException()
438			if self.refValue >= self.x_f:
439			raise RefException()
440
441			def eval(self, A):
442			r"""Evaluate solution.
443
444			Args:
445			A (numpy.ndarray): Solution to evaluate.
446
447			Returns:
448			float: Function/fitness values of solution.
449
450			See Also:
451			* :func:`NiaPy.util.ThrowingTask.stopCondE`
452			* :func:`NiaPy.util.StoppingTask.eval`
453
454			"""
455
456			self.stopCondE()
457			return StoppingTask.eval(self, A)
458
459
460			class TaskConvPlot():
461			r"""Task class with ability of showing convergence graph.
462
463			Attributes:
464			iters (List[int]): List of ints representing when the new global best was found.
465			x_fs (List[float]): List of floats representing function/fitness values found.
466
467			See Also:
468			* :class:`NiaPy.util.StoppingTask`
469
470			"""
471
472			def __init__(self, **kwargs):
473			r"""TODO.
474
475			Args:
476			**kwargs (Dict[str, Any]): Additional arguments.
477
478			See Also:
479			* :func:`NiaPy.util.StoppingTask.__init__`
480
481			"""
482
483			StoppingTask.__init__(self, **kwargs)
484			self.fig = plt.figure()
485			self.ax = self.fig.subplots(nrows=1, ncols=1)
486			self.ax.set_xlim(0, self.nFES)
487			self.line, = self.ax.plot(self.iters, self.x_fs, animated=True)
488			self.ani = anim.FuncAnimation(self.fig, self.updatePlot, blit=True)
489			self.showPlot()
490
491			def eval(self, A):
492			r"""Evaluate solution.
493
494			Args:
495			A (numpy.ndarray): Solution to evaluate.
496
497			Returns:
498			float: Fitness/function values of solution.
499
500			"""
501
502			x_f = StoppingTask.eval(self, A)
503			if not self.x_f_vals:
504			self.x_f_vals.append(x_f)
505			elif x_f < self.x_f_vals[-1]:
506			self.x_f_vals.append(x_f)
507			else:
508			self.x_f_vals.append(self.x_f_vals[-1])
509			self.evals.append(self.Evals)
510			return x_f
511
512			def showPlot(self):
513			r"""Animation updating function."""
514			plt.show(block=False)
515			plt.pause(0.001)
516
517			def updatePlot(self, frame):
518			r"""Update mathplotlib figure.
519
520			Args:
521			frame (): TODO
522
523			Returns:
524			Tuple[List[float], Any]:
525			1. Line
526
527			"""
528
529			if self.x_f_vals:
530			max_fs, min_fs = self.x_f_vals[0], self.x_f_vals[-1]
531			self.ax.set_ylim(min_fs + 1, max_fs + 1)
532			self.line.set_data(self.evals, self.x_f_vals)
533			return self.line,
534

NiaOrg / NiaPy

Pull Request — master (#209)

NiaPy.task.task.Task.__init__() C

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NiaPy.task.task.Task.init() C