def test_should_return_best_fitness_action(self, cfg):
# given
population = ClassifiersList(cfg=cfg)
# when & then
# C1 - does not anticipate change
c1 = Classifier(action=1, cfg=cfg)
population.append(c1)
# Some random action should be selected here
best_action = exploit(population)
assert best_action is not None
# when & then
# C2 - does anticipate some change
c2 = Classifier(action=2, effect='1###0###', reward=0.25, cfg=cfg)
population.append(c2)
# Here C2 action should be selected
best_action = exploit(population)
assert 2 == best_action
# when & then
# C3 - does anticipate change and is quite good
c3 = Classifier(action=3,
effect='1#######',
quality=0.8,
reward=5,
cfg=cfg)
population.append(c3)
# Here C3 has the biggest fitness score
best_action = exploit(population)
assert 3 == best_action
def test_should_exploit_with_single_classifier(self, cfg):
# given
cl = Classifier(action=2, effect='1###0###', reward=0.25, cfg=cfg)
population = ClassifiersList(*[cl])
# when
action = exploit(population, cfg.number_of_possible_actions)
# then
assert action == 2
def test_should_exploit_when_no_effect_specified(self, cfg):
# given a classifier not anticipating change
cl = Classifier(action=1, cfg=cfg)
population = ClassifiersList(*[cl])
# when
action = exploit(population, cfg.number_of_possible_actions)
# then random action is returned
assert action is not None
def test_should_exploit_using_majority_voting(self, cfg):
# given
cl1 = Classifier(action=1, effect='1###0###',
reward=0.1, quality=0.7, numerosity=9, cfg=cfg)
cl2 = Classifier(action=2, effect='1###0###',
reward=0.1, quality=0.7, numerosity=10, cfg=cfg)
population = ClassifiersList(*[cl1, cl2])
# when
action = exploit(population, cfg.number_of_possible_actions)
# then
assert action == 2