/usr/share/pyshared/pyevolve/GPopulation.py is in python-pyevolve 0.6~rc1+svn398+dfsg-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 | """
:mod:`GPopulation` -- the population module
================================================================
This module contains the :class:`GPopulation.GPopulation` class, which is reponsible
to keep the population and the statistics.
Default Parameters
-------------------------------------------------------------
*Sort Type*
>>> Consts.sortType["scaled"]
The scaled sort type
*Minimax*
>>> Consts.minimaxType["maximize"]
Maximize the evaluation function
*Scale Method*
:func:`Scaling.LinearScaling`
The Linear Scaling scheme
Class
-------------------------------------------------------------
"""
import Consts, Util
from FunctionSlot import FunctionSlot
from Statistics import Statistics
from math import sqrt as math_sqrt
import logging
try:
from multiprocessing import cpu_count, Pool
CPU_COUNT = cpu_count()
MULTI_PROCESSING = True if CPU_COUNT > 1 else False
logging.debug("You have %d CPU cores, so the multiprocessing state is %s", CPU_COUNT, MULTI_PROCESSING)
except ImportError:
MULTI_PROCESSING = False
logging.debug("You don't have multiprocessing support for your Python version !")
def key_raw_score(individual):
""" A key function to return raw score
:param individual: the individual instance
:rtype: the individual raw score
.. note:: this function is used by the max()/min() python functions
"""
return individual.score
def key_fitness_score(individual):
""" A key function to return fitness score, used by max()/min()
:param individual: the individual instance
:rtype: the individual fitness score
.. note:: this function is used by the max()/min() python functions
"""
return individual.fitness
def multiprocessing_eval(ind):
""" Internal used by the multiprocessing """
ind.evaluate()
return ind.score
def multiprocessing_eval_full(ind):
""" Internal used by the multiprocessing (full copy)"""
ind.evaluate()
return ind
class GPopulation:
""" GPopulation Class - The container for the population
**Examples**
Get the population from the :class:`GSimpleGA.GSimpleGA` (GA Engine) instance
>>> pop = ga_engine.getPopulation()
Get the best fitness individual
>>> bestIndividual = pop.bestFitness()
Get the best raw individual
>>> bestIndividual = pop.bestRaw()
Get the statistics from the :class:`Statistics.Statistics` instance
>>> stats = pop.getStatistics()
>>> print stats["rawMax"]
10.4
Iterate, get/set individuals
>>> for ind in pop:
>>> print ind
(...)
>>> for i in xrange(len(pop)):
>>> print pop[i]
(...)
>>> pop[10] = newGenome
>>> pop[10].fitness
12.5
:param genome: the :term:`Sample genome`, or a GPopulation object, when cloning.
"""
def __init__(self, genome):
""" The GPopulation Class creator """
if isinstance(genome, GPopulation):
self.oneSelfGenome = genome.oneSelfGenome
self.internalPop = []
self.internalPopRaw = []
self.popSize = genome.popSize
self.sortType = genome.sortType
self.sorted = False
self.minimax = genome.minimax
self.scaleMethod = genome.scaleMethod
self.allSlots = [self.scaleMethod]
self.internalParams = genome.internalParams
self.multiProcessing = genome.multiProcessing
self.statted = False
self.stats = Statistics()
return
logging.debug("New population instance, %s class genomes.", genome.__class__.__name__)
self.oneSelfGenome = genome
self.internalPop = []
self.internalPopRaw = []
self.popSize = 0
self.sortType = Consts.CDefPopSortType
self.sorted = False
self.minimax = Consts.CDefPopMinimax
self.scaleMethod = FunctionSlot("Scale Method")
self.scaleMethod.set(Consts.CDefPopScale)
self.allSlots = [self.scaleMethod]
self.internalParams = {}
self.multiProcessing = (False, False)
# Statistics
self.statted = False
self.stats = Statistics()
def setMultiProcessing(self, flag=True, full_copy=False):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
.. warning:: Use this option only when your evaluation function is slow, se you
will get a good tradeoff between the process communication speed and the
parallel evaluation.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
self.multiProcessing = (flag, full_copy)
def setMinimax(self, minimax):
""" Sets the population minimax
Example:
>>> pop.setMinimax(Consts.minimaxType["maximize"])
:param minimax: the minimax type
"""
self.minimax = minimax
def __repr__(self):
""" Returns the string representation of the population """
ret = "- GPopulation\n"
ret += "\tPopulation Size:\t %d\n" % (self.popSize,)
ret += "\tSort Type:\t\t %s\n" % (Consts.sortType.keys()[Consts.sortType.values().index(self.sortType)].capitalize(),)
ret += "\tMinimax Type:\t\t %s\n" % (Consts.minimaxType.keys()[Consts.minimaxType.values().index(self.minimax)].capitalize(),)
for slot in self.allSlots:
ret+= "\t" + slot.__repr__()
ret+="\n"
ret+= self.stats.__repr__()
return ret
def __len__(self):
""" Return the length of population """
return len(self.internalPop)
def __getitem__(self, key):
""" Returns the specified individual from population """
return self.internalPop[key]
def __iter__(self):
""" Returns the iterator of the population """
return iter(self.internalPop)
def __setitem__(self, key, value):
""" Set an individual of population """
self.internalPop[key] = value
self.clearFlags()
def clearFlags(self):
""" Clear the sorted and statted internal flags """
self.sorted = False
self.statted = False
def getStatistics(self):
""" Return a Statistics class for statistics
:rtype: the :class:`Statistics.Statistics` instance
"""
self.statistics()
return self.stats
def statistics(self):
""" Do statistical analysis of population and set 'statted' to True """
if self.statted: return
logging.debug("Running statistical calculations")
raw_sum = 0
fit_sum = 0
len_pop = len(self)
for ind in xrange(len_pop):
raw_sum += self[ind].score
#fit_sum += self[ind].fitness
self.stats["rawMax"] = max(self, key=key_raw_score).score
self.stats["rawMin"] = min(self, key=key_raw_score).score
self.stats["rawAve"] = raw_sum / float(len_pop)
#self.stats["rawTot"] = raw_sum
#self.stats["fitTot"] = fit_sum
tmpvar = 0.0
for ind in xrange(len_pop):
s = self[ind].score - self.stats["rawAve"]
s*= s
tmpvar += s
tmpvar/= float((len(self) - 1))
try:
self.stats["rawDev"] = math_sqrt(tmpvar)
except:
self.stats["rawDev"] = 0.0
self.stats["rawVar"] = tmpvar
self.statted = True
def bestFitness(self, index=0):
""" Return the best scaled fitness individual of population
:param index: the *index* best individual
:rtype: the individual
"""
self.sort()
return self.internalPop[index]
def bestRaw(self, index=0):
""" Return the best raw score individual of population
:param index: the *index* best raw individual
:rtype: the individual
.. versionadded:: 0.6
The parameter `index`.
"""
if self.sortType == Consts.sortType["raw"]:
return self.internalPop[index]
else:
self.sort()
return self.internalPopRaw[index]
def sort(self):
""" Sort the population """
if self.sorted: return
rev = (self.minimax == Consts.minimaxType["maximize"])
if self.sortType == Consts.sortType["raw"]:
self.internalPop.sort(cmp=Util.cmp_individual_raw, reverse=rev)
else:
self.scale()
self.internalPop.sort(cmp=Util.cmp_individual_scaled, reverse=rev)
self.internalPopRaw = self.internalPop[:]
self.internalPopRaw.sort(cmp=Util.cmp_individual_raw, reverse=rev)
self.sorted = True
def setPopulationSize(self, size):
""" Set the population size
:param size: the population size
"""
self.popSize = size
def setSortType(self, sort_type):
""" Sets the sort type
Example:
>>> pop.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
self.sortType = sort_type
def create(self, **args):
""" Clone the example genome to fill the population """
self.minimax = args["minimax"]
self.internalPop = [self.oneSelfGenome.clone() for i in xrange(self.popSize)]
self.clearFlags()
def __findIndividual(self, individual, end):
for i in xrange(end):
if individual.compare(self.internalPop[i]) == 0:
return True
def initialize(self, **args):
""" Initialize all individuals of population,
this calls the initialize() of individuals """
logging.debug("Initializing the population")
if self.oneSelfGenome.getParam("full_diversity", True) and hasattr(self.oneSelfGenome, "compare"):
for i in xrange(len(self.internalPop)):
curr = self.internalPop[i]
curr.initialize(**args)
while self.__findIndividual(curr, i):
curr.initialize(**args)
else:
for gen in self.internalPop:
gen.initialize(**args)
self.clearFlags()
def evaluate(self, **args):
""" Evaluate all individuals in population, calls the evaluate() method of individuals
:param args: this params are passed to the evaluation function
"""
# We have multiprocessing
if self.multiProcessing[0] and MULTI_PROCESSING:
logging.debug("Evaluating the population using the multiprocessing method")
proc_pool = Pool()
# Multiprocessing full_copy parameter
if self.multiProcessing[1]:
results = proc_pool.map(multiprocessing_eval_full, self.internalPop)
for i in xrange(len(self.internalPop)):
self.internalPop[i] = results[i]
else:
results = proc_pool.map(multiprocessing_eval, self.internalPop)
for individual, score in zip(self.internalPop, results):
individual.score = score
else:
for ind in self.internalPop:
ind.evaluate(**args)
self.clearFlags()
def scale(self, **args):
""" Scale the population using the scaling method
:param args: this parameter is passed to the scale method
"""
for it in self.scaleMethod.applyFunctions(self, **args):
pass
fit_sum = 0
for ind in xrange(len(self)):
fit_sum += self[ind].fitness
self.stats["fitMax"] = max(self, key=key_fitness_score).fitness
self.stats["fitMin"] = min(self, key=key_fitness_score).fitness
self.stats["fitAve"] = fit_sum / float(len(self))
self.sorted = False
def printStats(self):
""" Print statistics of the current population """
message = ""
if self.sortType == Consts.sortType["scaled"]:
message = "Max/Min/Avg Fitness(Raw) [%(fitMax).2f(%(rawMax).2f)/%(fitMin).2f(%(rawMin).2f)/%(fitAve).2f(%(rawAve).2f)]" % self.stats
else:
message = "Max/Min/Avg Raw [%(rawMax).2f/%(rawMin).2f/%(rawAve).2f]" % self.stats
logging.info(message)
print message
return message
def copy(self, pop):
""" Copy current population to 'pop'
:param pop: the destination population
.. warning:: this method do not copy the individuals, only the population logic
"""
pop.popSize = self.popSize
pop.sortType = self.sortType
pop.minimax = self.minimax
pop.scaleMethod = self.scaleMethod
#pop.internalParams = self.internalParams.copy()
pop.internalParams = self.internalParams
pop.multiProcessing = self.multiProcessing
def getParam(self, key, nvl=None):
""" Gets an internal parameter
Example:
>>> population.getParam("tournamentPool")
5
:param key: the key of param
:param nvl: if the key doesn't exist, the nvl will be returned
"""
return self.internalParams.get(key, nvl)
def setParams(self, **args):
""" Gets an internal parameter
Example:
>>> population.setParams(tournamentPool=5)
:param args: parameters to set
.. versionadded:: 0.6
The `setParams` method.
"""
self.internalParams.update(args)
def clear(self):
""" Remove all individuals from population """
del self.internalPop[:]
del self.internalPopRaw[:]
self.clearFlags()
def clone(self):
""" Return a brand-new cloned population """
newpop = GPopulation(self.oneSelfGenome)
self.copy(newpop)
return newpop
|