def test_should_find_subsumer_among_nonsubsumers(self, acs2_cfg):
# given
subsumer = acs2.Classifier(condition='###0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=acs2_cfg)
nonsubsumer = acs2.Classifier(action=3, cfg=acs2_cfg)
cl = acs2.Classifier(condition='1##0####',
action=3,
effect='##1#####',
quality=0.5,
reward=0.35,
experience=1,
cfg=acs2_cfg)
population = acs2.ClassifiersList(
*[nonsubsumer, subsumer, nonsubsumer])
# when
subsumers = find_subsumers(cl, population, acs2_cfg.theta_exp)
# then
assert len(subsumers) == 1
assert subsumers[0] == subsumer
def test_find_old_classifier_similar_and_subsumer_subsumer_returned(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
subsumer = acs2.Classifier(
condition='1#######',
action=1,
experience=21,
quality=0.95,
cfg=cfg)
similar = acs2.Classifier(
condition='10######',
action=1,
cfg=cfg)
population = acs2.ClassifiersList(*[similar, subsumer])
cl = acs2.Classifier(
condition='10######',
action=1,
cfg=cfg)
# when
old_cls = ga._find_old_classifier(population, cl, True, cfg.theta_exp)
# then
assert similar == cl
assert subsumer == old_cls
def test_should_add_classifier(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
cl_1 = acs2.Classifier(action=1, cfg=cfg)
cl_3 = acs2.Classifier(action=3, cfg=cfg)
cl_4 = acs2.Classifier(action=4, cfg=cfg)
population = acs2.ClassifiersList(*[cl_1, cl_3, cl_4])
match_set = acs2.ClassifiersList()
action_set = acs2.ClassifiersList(*[cl_1])
p0 = Perception('10101010')
cl = acs2.Classifier(
action=1,
condition='1#######',
cfg=cfg)
# when
ga.add_classifier(cl, p0,
population, match_set, action_set,
do_subsumption=True, theta_exp=cfg.theta_exp)
# then
assert acs2.ClassifiersList(*[cl_1, cl_3, cl_4, cl]) == population
assert acs2.ClassifiersList(*[cl]) == match_set
assert acs2.ClassifiersList(*[cl_1, cl]) == action_set
def test_add_ga_classifier_increase_numerosity(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
cl_1 = acs2.Classifier(action=2, condition='1#######', cfg=cfg)
cl_2 = acs2.Classifier(action=3, cfg=cfg)
cl_3 = acs2.Classifier(action=4, cfg=cfg)
population = acs2.ClassifiersList(*[cl_1, cl_2, cl_3])
match_set = acs2.ClassifiersList(*[cl_1])
action_set = acs2.ClassifiersList(*[cl_1])
cl = acs2.Classifier(action=2, condition='1#######', cfg=cfg)
# when
p0 = Perception('10101010')
ga.add_classifier(cl, p0,
population, match_set, action_set,
do_subsumption=True, theta_exp=cfg.theta_exp)
# then
assert cl_1.num == 2
assert acs2.ClassifiersList(*[cl_1, cl_2, cl_3]) == population
assert acs2.ClassifiersList(*[cl_1]) == match_set
assert acs2.ClassifiersList(*[cl_1]) == action_set
def test_should_find_old_classifier_only_subsumer(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
subsumer1 = acs2.Classifier(
condition='1##0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=cfg)
subsumer2 = acs2.Classifier(
condition='#1#0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=cfg)
most_general = acs2.Classifier(
condition='###0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=cfg)
nonsubsumer = acs2.Classifier(cfg=cfg)
cl = acs2.Classifier(
condition='11#0####',
action=3,
effect='##1#####',
quality=0.5,
reward=0.35,
experience=1,
cfg=cfg)
population = acs2.ClassifiersList(
*[nonsubsumer, subsumer1, nonsubsumer, most_general,
subsumer2, nonsubsumer])
# when
old_cl = ga._find_old_classifier(population, cl, True, cfg.theta_exp)
# then
assert old_cl == most_general
def test_two_point_crossover(self, _seed, _c1, _c2, _rc1, _rc2):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
cl1 = acs2.Classifier(condition=_c1, cfg=cfg)
cl2 = acs2.Classifier(condition=_c2, cfg=cfg)
# when
np.random.seed(_seed)
ga.two_point_crossover(cl1, cl2)
# then
assert cl1.condition == acs.Condition(_rc1)
assert cl2.condition == acs.Condition(_rc2)
def test_should_delete_ga_classifier(self):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
clss = []
# Total of 20 micro-classifiers
action_cl_num_fixtures = {
1: [1, 5, 4],
2: [1, 1, 6, 2]
}
for action, num_lst in action_cl_num_fixtures.items():
for num in num_lst:
clss.append(acs2.Classifier(
action=action, numerosity=num, cfg=cfg))
insize = 2 # space needed for new classifiers
theta_as = 10 # maximum size of the action set
# when
population = acs2.ClassifiersList(*clss)
action_set = population.form_action_set(1)
ga.delete_classifiers(population, None, action_set, insize, theta_as)
# then
assert sum(cl.num for cl in population) == 18
assert sum(cl.num for cl in action_set) == 8
def test_should_distinguish_classifier_as_subsumer(self, _exp, _q,
_is_subsumer, acs2_cfg):
# given
cl = acs2.Classifier(experience=_exp, quality=_q, cfg=acs2_cfg)
# when & then
# general classifier should not be considered as subsumer
assert is_subsumer(cl, acs2_cfg.theta_exp) is _is_subsumer
def test_should_find_most_general_subsumer(self, acs2_cfg):
# given
subsumer1 = acs2.Classifier(condition='1##0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=acs2_cfg)
subsumer2 = acs2.Classifier(condition='#1#0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=acs2_cfg)
most_general = acs2.Classifier(condition='###0####',
action=3,
effect='##1#####',
quality=0.93,
reward=1.35,
experience=23,
cfg=acs2_cfg)
nonsubsumer = acs2.Classifier(cfg=acs2_cfg)
cl = acs2.Classifier(condition='11#0####',
action=3,
effect='##1#####',
quality=0.5,
reward=0.35,
experience=1,
cfg=acs2_cfg)
population = acs2.ClassifiersList(*[
nonsubsumer, subsumer1, nonsubsumer, most_general, subsumer2,
nonsubsumer
])
# when
subsumers = find_subsumers(cl, population, acs2_cfg.theta_exp)
# then
assert subsumers[0] == most_general
def test_should_not_distinguish_marked_classifier_as_subsumer(
self, acs2_cfg):
# given
# Now check if the fact that classifier is marked will block
# it from being considered as a subsumer
cl = acs2.Classifier(experience=30, quality=0.92, cfg=acs2_cfg)
cl.mark[3].add('1')
# when & then
assert is_subsumer(cl, acs2_cfg.theta_exp) is False
def test_find_old_classifier_only_similar(self):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
cl_1 = acs2.Classifier(action=1, experience=32, cfg=cfg)
cl_2 = acs2.Classifier(action=1, cfg=cfg)
population = acs2.ClassifiersList(
*[cl_1,
acs2.Classifier(action=2, cfg=cfg),
acs2.Classifier(action=3, cfg=cfg),
cl_2])
# when
cl = acs2.Classifier(action=1, cfg=cfg)
old_cl = ga._find_old_classifier(population, cl, True, cfg.theta_exp)
# then
assert old_cl == cl_1
def test_generalizing_mutation(self, _mu, _cond1, _cond2):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
cl = acs2.Classifier(condition=_cond1, cfg=cfg)
# when
ga.generalizing_mutation(cl, mu=_mu)
# then
assert cl.condition == acs.Condition(_cond2)
def test_should_not_find_old_classifier(self):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
cl = acs2.Classifier(cfg=cfg)
population = acs2.ClassifiersList()
# when
old_cl = ga._find_old_classifier(population, cl, True, cfg.theta_exp)
# then
assert old_cl is None
def test_should_work_with_acs2(self, acs2_cfg):
# given
c1 = acs2.Classifier(condition='1##1',
action=0,
effect='0###',
quality=0.571313,
reward=7.67011,
cfg=acs2_cfg)
c2 = acs2.Classifier(condition='1##1',
action=1,
effect='0###',
quality=0.581313,
reward=7.67011,
cfg=acs2_cfg)
population = acs2.ClassifiersList(*[c1, c2])
# when
eg = EpsilonGreedy(acs2_cfg.number_of_possible_actions, epsilon=0.0)
best_action = eg(population)
# then
assert best_action == 1
def test_should_subsume_another_classifier(self, _cl1c, _cl1a, _cl1e,
_cl1q, _cl1r, _cl1exp, _cl2c,
_cl2a, _cl2e, _cl2q, _cl2r,
_cl2exp, _result, acs2_cfg):
# given
cl = acs2.Classifier(condition=_cl1c,
action=_cl1a,
effect=_cl1e,
quality=_cl1q,
reward=_cl1r,
experience=_cl1exp,
cfg=acs2_cfg)
other = acs2.Classifier(condition=_cl2c,
action=_cl2a,
effect=_cl2e,
quality=_cl2q,
reward=_cl2r,
experience=_cl2exp,
cfg=acs2_cfg)
# when & then
assert does_subsume(cl, other, acs2_cfg.theta_exp) is _result
def test_should_insert_alp_offspring_1(self):
# given
cfg = acs2.Configuration(8, 8)
population = acs2.ClassifiersList()
new_list = acs2.ClassifiersList()
child = acs2.Classifier(
condition='1##1#010',
action=0,
effect='0####101',
quality=0.5,
reward=8.96245,
immediate_reward=0,
experience=1,
tga=423,
talp=423,
tav=27.3182,
cfg=cfg
)
c1 = acs2.Classifier(
condition='1##1#010',
action=0,
effect='0####101',
quality=0.571313,
reward=7.67011,
immediate_reward=0,
experience=3,
tga=225,
talp=423,
tav=70.881,
cfg=cfg
)
c2 = acs2.Classifier(
condition='1####010',
action=0,
effect='0####101',
quality=0.462151,
reward=8.96245,
immediate_reward=0,
experience=11,
tga=143,
talp=423,
tav=27.3182,
cfg=cfg
)
c3 = acs2.Classifier(
condition='1####0##',
action=0,
effect='0####1##',
quality=0.31452,
reward=9.04305,
immediate_reward=0,
experience=19,
tga=49,
talp=423,
tav=19.125,
cfg=cfg
)
population.extend([c1, c2, c3])
# when
alp.add_classifier(child, population, new_list, cfg.theta_exp)
# then
assert len(population) == 3
assert c1 in population
assert c2 in population
assert c3 in population
assert abs(0.592747 - c1.q) < 0.01
def test_should_insert_alp_offspring_2(self):
# given
cfg = acs2.Configuration(8, 8)
population = acs2.ClassifiersList()
new_list = acs2.ClassifiersList()
child = acs2.Classifier(
condition='#1O##O##',
action=0,
reward=18.206,
immediate_reward=0,
experience=1,
tga=747,
talp=747,
tav=22.0755,
cfg=cfg
)
c1 = acs2.Classifier(
condition='#1O#O###',
action=0,
quality=0.650831,
reward=14.8323,
immediate_reward=0,
experience=5,
tga=531,
talp=747,
tav=48.3562,
cfg=cfg
)
c2 = acs2.Classifier(
condition='##O#O###',
action=0,
quality=0.79094,
reward=9.97782,
immediate_reward=0,
experience=10,
tga=330,
talp=747,
tav=43.7171,
cfg=cfg
)
c3 = acs2.Classifier(
condition='#1O###1O',
action=0,
effect='#O1####1',
quality=0.515369,
reward=8.3284,
immediate_reward=0,
experience=8,
tga=316,
talp=747,
tav=57.8883,
cfg=cfg
)
c3.mark[0].update(['1'])
c3.mark[3].update(['0'])
c3.mark[4].update(['0'])
c3.mark[5].update(['0'])
c4 = acs2.Classifier(
condition='####O###',
action=0,
quality=0.903144,
reward=14.8722,
immediate_reward=0,
experience=25,
tga=187,
talp=747,
tav=23.0038,
cfg=cfg
)
c5 = acs2.Classifier(
condition='#1O####O',
action=0,
effect='#O1####1',
quality=0.647915,
reward=9.24712,
immediate_reward=0,
experience=14,
tga=154,
talp=747,
tav=44.5457,
cfg=cfg
)
c5.mark[0].update(['1'])
c5.mark[3].update(['0', '1'])
c5.mark[4].update(['0', '1'])
c5.mark[5].update(['0', '1'])
c5.mark[6].update(['0', '1'])
c6 = acs2.Classifier(
condition='#1O#####',
action=0,
quality=0.179243,
reward=18.206,
immediate_reward=0,
experience=29,
tga=104,
talp=747,
tav=22.0755,
cfg=cfg
)
c6.mark[0].update(['1'])
c6.mark[3].update(['1'])
c6.mark[4].update(['1'])
c6.mark[5].update(['1'])
c6.mark[6].update(['0', '1'])
c6.mark[7].update(['0', '1'])
c7 = acs2.Classifier(
condition='##O#####',
action=0,
quality=0.100984,
reward=15.91,
immediate_reward=0,
experience=44,
tga=58,
talp=747,
tav=14.4171,
cfg=cfg
)
c7.mark[0].update(['0', '1'])
c7.mark[1].update(['0', '1'])
c7.mark[3].update(['0', '1'])
c7.mark[5].update(['0', '1'])
c7.mark[6].update(['0', '1'])
c7.mark[7].update(['0', '1'])
# Add classifiers into current ClassifierList
population.extend([c1, c2, c3, c4, c5, c6, c7])
# When
alp.add_classifier(child, population, new_list, cfg.theta_exp)
# Then
assert 7 == len(population)
assert 0 == len(new_list)
assert c1 in population
assert c2 in population
assert c3 in population
assert c4 in population
assert c5 in population
assert c6 in population
assert c7 in population
# `C4` should be subsumer of `child`
assert abs(0.907987 - c4.q) < 0.01
