def test_dominates(self):
print(">> ContinuumRule.dominates(self, rule)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
var = random.randint(0, len(variables) - 1)
r = self.random_rule(variables, domains)
# Undominated rule: var = emptyset if anything
r0 = ContinuumRule(r.get_head_variable(), Continuum())
self.assertTrue(r0.dominates(r0))
# r1 is a regular rule
r1 = self.random_rule(variables, domains)
while r1.size() == 0:
r1 = self.random_rule(variables, domains)
var = r.get_head_variable()
val = Continuum.random(domains[var].get_min_value(),
domains[var].get_max_value())
r1 = ContinuumRule(var, val, r1.get_body()) # set head
self.assertTrue(r0.dominates(r1))
self.assertFalse(r1.dominates(r0))
# r2 is precision of r1 (head specification)
r2 = r1.copy()
var = r2.get_head_variable()
val = r2.get_head_value()
while r2.get_head_value(
) == val or not r2.get_head_value().includes(val):
val = Continuum.random(val.get_min_value(),
val.get_max_value())
r2.set_head_value(val)
self.assertEqual(r0.dominates(r2), True)
self.assertEqual(r2.dominates(r0), False)
self.assertEqual(r2.dominates(r1), True)
self.assertEqual(r1.dominates(r2), False)
# r3 is a generalization of r1 (body generalization)
r3 = r1.copy()
var, val = random.choice(r3.get_body())
val_ = val
modif_min = random.uniform(0, self.__max_value)
modif_max = random.uniform(0, self.__max_value)
while val_ == val or not val_.includes(val):
val_ = Continuum.random(val.get_min_value() - modif_min,
val.get_max_value() + modif_max)
r3.set_condition(var, val_)
self.assertEqual(r0.dominates(r3), True)
self.assertEqual(r3.dominates(r0), False)
self.assertEqual(r3.dominates(r1), True)
self.assertEqual(r1.dominates(r3), False)
# r4 is unprecision of r1 (head generalization)
r4 = r1.copy()
var = r4.get_head_variable()
val = r4.get_head_value()
modif_min = random.uniform(0, self.__max_value)
modif_max = random.uniform(0, self.__max_value)
while r4.get_head_value() == val or not val.includes(
r4.get_head_value()):
val = Continuum(val.get_min_value() - modif_min,
val.get_max_value() + modif_max,
random.choice([True, False]),
random.choice([True, False]))
r4.set_head_value(val)
self.assertEqual(r0.dominates(r4), True)
self.assertEqual(r4.dominates(r0), False)
self.assertEqual(r4.dominates(r1), False)
self.assertEqual(r1.dominates(r4), True)
# r5 is specialization of r1 (body specialization)
r5 = r1.copy()
var, val = random.choice(r5.get_body())
val_ = val
while val_ == val or not val.includes(val_):
val_ = Continuum.random(val.get_min_value(),
val.get_max_value())
r5.set_condition(var, val_)
self.assertEqual(r0.dominates(r5), True)
self.assertEqual(r5.dominates(r0), False)
self.assertEqual(r5.dominates(r1), False)
self.assertEqual(r1.dominates(r5), True)
# r6 is a random rule
r6 = self.random_rule(variables, domains)
# head var difference
if r6.get_head_variable() != r1.get_head_variable():
self.assertFalse(r6.dominates(r1))
self.assertFalse(r1.dominates(r6))
r6 = ContinuumRule(r1.get_head_variable(), r6.get_head_value(),
r6.get_body()) # same head var
#eprint("r1: ", r1)
#eprint("r6: ", r6)
# head val inclusion
if not r1.get_head_value().includes(r6.get_head_value()):
self.assertFalse(r6.dominates(r1))
r6 = ContinuumRule(r1.get_head_variable(), r1.get_head_value(),
r6.get_body()) # same head var, same head val
# body subsumption
if r1.dominates(r6):
for var, val in r1.get_body():
self.assertTrue(val.includes(r6.get_condition(var)))
# body subsumption
if r6.dominates(r1):
for var, val in r6.get_body():
self.assertTrue(val.includes(r1.get_condition(var)))
# incomparable
if not r1.dominates(r6) and not r6.dominates(r1):
#eprint("r1: ", r1)
#eprint("r6: ", r6)
conflicts = False
dominant_r1 = False
dominant_r6 = False
for var, val in r1.get_body():
# condition not appearing
if not r6.has_condition(var):
conflicts = True
break
# value not included
if not r6.get_condition(var).includes(
val) and not val.includes(r6.get_condition(var)):
conflicts = True
break
# local dominates
if val.includes(r6.get_condition(var)):
dominant_r1 = True
# local dominated
if r6.get_condition(var).includes(val):
if dominant_r1:
conflicts = True
break
for var, val in r6.get_body():
# condition not appearing
if not r1.has_condition(var):
conflicts = True
break
# value not included
if not r1.get_condition(var).includes(
val) and not val.includes(r1.get_condition(var)):
conflicts = True
break
# local dominates
if val.includes(r1.get_condition(var)):
dominant_r6 = True
if dominant_r1:
conflicts = True
break
# local dominated
if r1.get_condition(var).includes(val):
if dominant_r6:
conflicts = True
break
self.assertTrue(conflicts)
def test_least_revision(self):
eprint(">> ACEDIA.least_revision(rule, state_1, state_2)")
for i in range(self.__nb_unit_test):
variables, domains = self.random_system()
state_1 = self.random_state(variables, domains)
state_2 = self.random_state(variables, domains)
# not matching
#--------------
rule = self.random_rule(variables, domains)
while rule.matches(state_1):
rule = self.random_rule(variables, domains)
self.assertRaises(ValueError, ACEDIA.least_revision, rule, state_1,
state_2)
# matching
#--------------
rule = self.random_rule(variables, domains)
while not rule.matches(state_1):
rule = self.random_rule(variables, domains)
head_var = rule.get_head_variable()
target_val = state_2[rule.get_head_variable()]
# Consistent
head_value = Continuum()
while not head_value.includes(target_val):
head_value = Continuum.random(
domains[head_var].get_min_value(),
domains[head_var].get_max_value())
rule.set_head_value(head_value)
self.assertRaises(ValueError, ACEDIA.least_revision, rule, state_1,
state_2)
# Empty set head
rule.set_head_value(Continuum())
LR = ACEDIA.least_revision(rule, state_1, state_2)
lg = rule.copy()
lg.set_head_value(Continuum(target_val, target_val, True, True))
self.assertTrue(lg in LR)
nb_valid_revision = 1
for var, val in rule.get_body():
state_value = state_1[var]
# min rev
ls = rule.copy()
new_val = val.copy()
new_val.set_lower_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
# max rev
ls = rule.copy()
new_val = val.copy()
new_val.set_upper_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
self.assertEqual(len(LR), nb_valid_revision)
#eprint(nb_valid_revision)
# usual head
head_value = Continuum.random(domains[head_var].get_min_value(),
domains[head_var].get_max_value())
while head_value.includes(target_val):
head_value = Continuum.random(
domains[head_var].get_min_value(),
domains[head_var].get_max_value())
rule.set_head_value(head_value)
LR = ACEDIA.least_revision(rule, state_1, state_2)
lg = rule.copy()
head_value = lg.get_head_value()
if target_val <= head_value.get_min_value():
head_value.set_lower_bound(target_val, True)
else:
head_value.set_upper_bound(target_val, True)
lg.set_head_value(head_value)
self.assertTrue(lg in LR)
nb_valid_revision = 1
for var, val in rule.get_body():
state_value = state_1[var]
# min rev
ls = rule.copy()
new_val = val.copy()
new_val.set_lower_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
# max rev
ls = rule.copy()
new_val = val.copy()
new_val.set_upper_bound(state_value, False)
if not new_val.is_empty():
ls.set_condition(var, new_val)
self.assertTrue(ls in LR)
nb_valid_revision += 1
self.assertEqual(len(LR), nb_valid_revision)
def test_includes(self):
eprint(">> Continuum.includes(self, element)")
for i in range(self.__nb_unit_test):
# bad argument type
c = Continuum.random(self.__min_value, self.__max_value)
self.assertRaises(TypeError, c.includes, "test")
# float argument
#----------------
# empty set includes nothing
c = Continuum()
value = random.uniform(self.__min_value, self.__max_value)
self.assertFalse(c.includes(value))
c = Continuum.random(self.__min_value, self.__max_value)
# Before min
value = c.get_min_value()
while value == c.get_min_value():
value = random.uniform(c.get_min_value() - 100.0,
c.get_min_value())
self.assertFalse(c.includes(value))
# on min bound
self.assertEqual(c.includes(c.get_min_value()), c.min_included())
# Inside
value = c.get_min_value()
while value == c.get_min_value() or value == c.get_max_value():
value = random.uniform(c.get_min_value(), c.get_max_value())
self.assertTrue(c.includes(value))
# on max bound
self.assertEqual(c.includes(c.get_max_value()), c.max_included())
# after max bound
value = c.get_max_value()
while value == c.get_max_value():
value = random.uniform(c.get_max_value(),
c.get_max_value() + 100.0)
self.assertFalse(c.includes(value))
# int argument
#--------------
# empty set includes nothing
c = Continuum()
value = random.randint(int(self.__min_value),
int(self.__max_value))
self.assertFalse(c.includes(value))
c = Continuum.random(self.__min_value, self.__max_value)
while int(c.get_max_value()) - int(c.get_min_value()) <= 1:
min = random.uniform(self.__min_value, self.__max_value)
max = random.uniform(min, self.__max_value)
c = Continuum.random(min, max)
#eprint(c.to_string())
# Before min
value = random.randint(int(c.get_min_value() - 100),
int(c.get_min_value()) - 1)
self.assertFalse(c.includes(value))
# on min bound
self.assertEqual(c.includes(c.get_min_value()), c.min_included())
# Inside
value = random.randint(
int(c.get_min_value()) + 1,
int(c.get_max_value()) - 1)
#eprint(value)
self.assertTrue(c.includes(value))
# on max bound
self.assertEqual(c.includes(c.get_max_value()), c.max_included())
# after max bound
value = random.randint(
int(c.get_max_value()) + 1, int(c.get_max_value() + 100))
self.assertFalse(c.includes(value))
# continuum argument
#--------------------
# 0) c is empty set
c = Continuum()
c_ = Continuum()
self.assertTrue(c.includes(c_)) # empty set VS empty set
c_ = Continuum.random(self.__min_value, self.__max_value)
while c_.is_empty():
c_ = Continuum.random(self.__min_value, self.__max_value)
self.assertFalse(c.includes(c_)) # empty set VS non empty
# 1) c is non empty
c = Continuum.random(self.__min_value, self.__max_value)
self.assertTrue(c.includes(Continuum())) # non empty VS empty set
self.assertTrue(c.includes(c)) # includes itself
# 1.1) Lower bound over
c_ = Continuum.random(c.get_min_value(), self.__max_value)
while c_.is_empty():
c_ = Continuum.random(c.get_min_value(), self.__max_value)
value = c.get_min_value()
while value == c.get_min_value():
value = random.uniform(c.get_min_value() - 100,
c.get_min_value())
c_.set_lower_bound(value, random.choice([True, False]))
self.assertFalse(c.includes(c_))
# 1.2) on min bound
c_ = Continuum.random(c.get_min_value(), self.__max_value)
while c_.is_empty():
c_ = Continuum.random(c.get_min_value(), self.__max_value)
c_.set_lower_bound(c.get_min_value(), random.choice([True, False]))
if not c.min_included() and c_.min_included(): # one value over
self.assertFalse(c.includes(c_))
# 1.3) upper bound over
c_ = Continuum.random(self.__min_value, c.get_max_value())
while c_.is_empty():
c_ = Continuum.random(self.__min_value, c.get_max_value())
value = c.get_max_value()
while value == c.get_max_value():
value = random.uniform(c.get_max_value(),
c.get_max_value() + 100)
c_.set_upper_bound(value, random.choice([True, False]))
self.assertFalse(c.includes(c_))
# 1.4) on upper bound
c_ = Continuum.random(self.__min_value, c.get_max_value())
while c_.is_empty():
c_ = Continuum.random(self.__min_value, c.get_max_value())
c_.set_upper_bound(c.get_max_value(), random.choice([True, False]))
if not c.max_included() and c_.max_included(): # one value over
self.assertFalse(c.includes(c_))
# 1.5) inside
min = c.get_min_value()
while min == c.get_min_value():
min = random.uniform(c.get_min_value(), c.get_max_value())
max = c.get_max_value()
while max == c.get_max_value():
max = random.uniform(min, c.get_max_value())
c_ = Continuum(min, max, random.choice([True, False]),
random.choice([True, False]))
self.assertTrue(c.includes(c_))
self.assertFalse(c_.includes(c))