"""Policy class implementation which is used for selecting operator using Upper Confidence Bound
"""
# main imports
import logging
import random
import math
import numpy as np
# module imports
from .Policy import Policy
[docs]class UCBPolicy(Policy):
"""UCB policy class which is used for applying UCB strategy when selecting and applying operator
Attributes:
operators: {[Operator]} -- list of selected operators for the algorithm
C: {float} -- tradeoff between EvE parameter for UCB
exp_rate: {float} -- exploration rate (probability to choose randomly next operator)
rewards: {[float]} -- list of summed rewards obtained for each operator
occurrences: {[int]} -- number of use (selected) of each operator
"""
def __init__(self, operators, C=100., exp_rate=0.5):
self._operators = operators
self._rewards = [0. for o in self._operators]
self._occurrences = [0 for o in self._operators]
self._C = C
self._exp_rate = exp_rate
[docs] def select(self):
"""Select randomly the next operator to use
Returns:
{Operator}: the selected operator
"""
indices = [i for i, o in enumerate(self._occurrences) if o == 0]
# random choice following exploration rate
if np.random.uniform(0, 1) <= self._exp_rate:
index = random.choice(range(len(self._operators)))
return self._operators[index]
elif len(indices) == 0:
# if operator have at least be used one time
ucbValues = []
nVisits = sum(self._occurrences)
for i in range(len(self._operators)):
ucbValue = self._rewards[i] + self._C * math.sqrt(
math.log(nVisits) / (self._occurrences[i] + 0.1))
ucbValues.append(ucbValue)
return self._operators[ucbValues.index(max(ucbValues))]
else:
return self._operators[random.choice(indices)]
[docs] def apply(self, solution):
"""
Apply specific operator chosen to create new solution, computes its fitness and returns solution
Args:
solution: {Solution} -- the solution to use for generating new solution
Returns:
{Solution} -- new generated solution
"""
operator = self.select()
logging.info("---- Applying %s on %s" %
(type(operator).__name__, solution))
# apply operator on solution
newSolution = operator.apply(solution)
# compute fitness of new solution
newSolution.evaluate(self._algo._evaluator)
# compute fitness improvment rate
if self._algo._maximise:
fir = (newSolution.fitness() -
solution.fitness()) / solution.fitness()
else:
fir = (solution.fitness() -
newSolution.fitness()) / solution.fitness()
operator_index = self._operators.index(operator)
if fir > 0:
self._rewards[operator_index] += fir
self._occurrences[operator_index] += 1
logging.info("---- Obtaining %s" % (solution))
return newSolution