def print_rule_hash():
DescriptionsTests.setUpClass()
rule = create_rule()
if os.getenv("HASH_RULE_TEST") == "1":
print(hash_rule(rule, DescriptionsTests.fe))
else:
print(describe_rule(rule, DescriptionsTests.fe))
def test_describe_rule_bag(self):
name, indices, feature = self.bag
activated = randint(0, len(indices) - 1)
activated_name = feature.names[activated]
not_activated = randint(0, len(indices) - 1)
not_activated_name = feature.names[not_activated]
picked_class = randint(0, self.n_classes - 1)
picked_class_name = self.class_representations[picked_class]
index = indices[activated]
not_index = indices[not_activated]
rule = Rule(attrs=[
RuleAttribute(index, True, 0.5),
RuleAttribute(not_index, False, 0.5)
],
stats=RuleStats(picked_class, 0.9, 150),
artificial=False)
self.assertEqual(
describe_rule(rule, self.fe), " %s in {%s} and not in {%s}\n"
"⇒ y = %s\n"
"Confidence: 0.900. Support: 150." % (
name,
activated_name,
not_activated_name,
picked_class_name,
))
def test_describe_rule_ordinal(self):
name, indices, feature = self.ordinal
picked_class = randint(0, self.n_classes - 1)
picked_class_name = self.class_representations[picked_class]
index = indices[0]
rule = Rule(attrs=[(index, True, 4.5)], stats=RuleStats(picked_class, 0.9, 150),
artificial=False)
self.assertEqual(describe_rule(rule, self.fe),
" %s ≥ %d\n"
"⇒ y = %s\n"
"Confidence: 0.900. Support: 150." % (
name, ceil(4.5), picked_class_name,
))
def test_describe_rule_parent(self):
feature_id = FeatureId.internal_type
index = self.fe.feature_to_indices[FeatureGroup.parents][0][feature_id][0]
picked_class = randint(0, self.n_classes - 1)
picked_class_name = self.class_representations[picked_class]
rule = Rule(attrs=[(index, True, 4)], stats=RuleStats(picked_class, 0.9, 150),
artificial=False)
self.assertEqual(describe_rule(rule, self.fe),
" ^1.%s = AnyTypeAnnotation\n"
"⇒ y = %s\n"
"Confidence: 0.900. Support: 150." % (
feature_id.name, picked_class_name,
))
def test_describe_rule_right(self):
feature_id = FeatureId.length
index = self.fe.feature_to_indices[FeatureGroup.right][0][feature_id][0]
picked_class = randint(0, self.n_classes - 1)
picked_class_name = self.class_representations[picked_class]
rule = Rule(attrs=[(index, True, 4.5)], stats=RuleStats(picked_class, 0.9, 150),
artificial=False)
self.assertEqual(describe_rule(rule, self.fe),
" +1.%s ≥ %d\n"
"⇒ y = %s\n"
"Confidence: 0.900. Support: 150." % (
feature_id.name, ceil(4.5), picked_class_name,
))
def test_describe_rule_categorical(self):
name, indices, feature = self.categorical
activated = randint(0, len(indices) - 1)
activated_name = feature.names[activated]
picked_class = randint(0, self.n_classes - 1)
picked_class_name = self.class_representations[picked_class]
index = indices[activated]
rule = Rule(attrs=[(index, True, 0.5)], stats=RuleStats(picked_class, 0.9, 150),
artificial=False)
self.assertEqual(describe_rule(rule, self.fe),
" %s = %s\n"
"⇒ y = %s\n"
"Confidence: 0.900. Support: 150." % (
name, activated_name, picked_class_name,
))
def test_hash_rule(self):
rule = create_rule()
my_hash = hash_rule(rule, self.fe)
self.assertEqual(len(my_hash), 8)
for c in my_hash:
self.assertIn(c, set("abcdef0123456789"))
env = os.environ.copy()
env["HASH_RULE_TEST"] = "1"
other_hash = subprocess.check_output([sys.executable, __file__],
env=env).decode().strip()
if my_hash != other_hash:
s1 = describe_rule(rule, self.fe)
env["HASH_RULE_TEST"] = "2"
s2 = subprocess.check_output([sys.executable, __file__],
env=env).decode().strip()
else:
s1 = s2 = ""
self.assertEqual(my_hash, other_hash,
"%s\n%s\n%s\n%s" % ("=" * 20, s1, "=" * 20, s2))
