def test_symmetry(self):
trees = itertools.product((randtree(5, repeat=3, width=2) for x in xrange(N)), repeat=2)
for a,b in trees:
ab = compare.distance(a,b)
ba = compare.distance(b,a)
#print '-----------------------------'
#print ab, ba
self.assertEquals(ab, ba)
def test_symmetry(self):
trees = itertools.product(
(randtree(5, repeat=3, width=2) for x in xrange(N)), repeat=2)
for a, b in trees:
ab = compare.distance(a, b)
ba = compare.distance(b, a)
#print '-----------------------------'
#print ab, ba
self.assertEquals(ab, ba)
def test_triangle_inequality(self):
trees = itertools.product((randtree(5, repeat=3, width=2) for x in xrange(N)), (randtree(5, repeat=3, width=2) for x in xrange(N)), (randtree(5, repeat=3, width=2) for x in xrange(N)))
for a,b,c in trees:
#print '--------------------------------'
ab = compare.distance(a,b)
bc = compare.distance(b,c)
ac = compare.distance(a,c)
#print ab, bc, ac
self.assertTrue(ac <= ab + bc)
def test_triangle_inequality(self):
trees = itertools.product(
(randtree(5, repeat=3, width=2) for x in xrange(N)),
(randtree(5, repeat=3, width=2) for x in xrange(N)),
(randtree(5, repeat=3, width=2) for x in xrange(N)))
for a, b, c in trees:
#print '--------------------------------'
ab = compare.distance(a, b)
bc = compare.distance(b, c)
ac = compare.distance(a, c)
#print ab, bc, ac
self.assertTrue(ac <= ab + bc)
def test_paper_tree():
A = (
Node("f")
.addkid(Node("d")
.addkid(Node("a"))
.addkid(Node("c")
.addkid(Node("b"))
)
)
.addkid(Node("e"))
)
B = (
Node("f")
.addkid(Node("c")
.addkid(Node("d")
.addkid(Node("a"))
.addkid(Node("b"))
)
)
.addkid(Node("e"))
)
#print A
#print
#print B
dist = compare.distance(A,B)
assert dist == 2
def test_wrong_index_regression():
A = Node('r')
B = (
Node("a")
.addkid(Node("b")
.addkid(Node("c"))
.addkid(Node("d"))
)
.addkid(Node("e")
.addkid(Node("f"))
.addkid(Node("g"))
)
)
def insert(n):
return ord(n.label[0])
def remove(n):
return ord(n.label[0])
def update(a,b):
return 10000*strdist(a.label, b.label)
dist = distance(A, B,
get_children=(lambda n: n.children),
insert_cost=insert,
remove_cost=remove,
update_cost=update,
)
assert dist == 814
def test_paper_tree():
Node = WeirdNode
A = (
Node("f")
.addkid(Node("d")
.addkid(Node("a"))
.addkid(Node("c")
.addkid(Node("b"))
)
)
.addkid(Node("e"))
)
B = (
Node("f")
.addkid(Node("c")
.addkid(Node("d")
.addkid(Node("a"))
.addkid(Node("b"))
)
)
.addkid(Node("e"))
)
#print A
#print
#print B
dist = compare.distance(A,B,WeirdNode.get_children, WeirdNode.get_label,
weird_dist)
assert dist == 20
def test_wrong_index_regression():
A = Node('r')
B = (
Node("a")
.addkid(Node("b")
.addkid(Node("c"))
.addkid(Node("d"))
)
.addkid(Node("e")
.addkid(Node("f"))
.addkid(Node("g"))
)
)
def insert(n):
return ord(n.label[0])
def remove(n):
return ord(n.label[0])
def update(a,b):
return 10000*strdist(a.label, b.label)
dist = distance(A, B,
get_children=(lambda n: n.children),
insert_cost=insert,
remove_cost=remove,
update_cost=update,
)
assert dist == 814
def test_nondegenercy(self):
trees = itertools.product((randtree(5, repeat=3, width=2) for x in xrange(N)), repeat=2)
for a,b in trees:
d = compare.distance(a,b)
#print '-----------------------------'
#print d, a is b
self.assertTrue((d == 0 and a is b) or a is not b)
def test_labelchange(self):
for A in (randtree(5, repeat=3, width=2) for x in xrange(N*4)):
B = copy.deepcopy(A)
node = random.choice([n for n in B.iter()])
old_label = str(node.label)
node.label = 'xty'
assert compare.distance(A, B) == compare.strdist(old_label, node.label)
def zssdist(t1, t2, names):
"""
t1: Tree, t2:Tree -> x
Return the edit distance, as computed by zss.compare, between t2zst-compatible trees t1, t2
"""
t1, t2 = map(lambda x: t2zst(x, names), [t1, t2])
return zscmp.distance(t1, t2)
def test_nondegenercy(self):
trees = itertools.product(
(randtree(5, repeat=3, width=2) for x in xrange(N)), repeat=2)
for a, b in trees:
d = compare.distance(a, b)
#print '-----------------------------'
#print d, a is b
self.assertTrue((d == 0 and a is b) or a is not b)
def test_labelchange(self):
for A in (randtree(5, repeat=3, width=2) for x in xrange(N * 4)):
B = copy.deepcopy(A)
node = random.choice([n for n in B.iter()])
old_label = str(node.label)
node.label = 'xty'
assert compare.distance(A, B) == compare.strdist(
old_label, node.label)
def test_simplelabelchange():
A = (Node("f").addkid(
Node("a").addkid(Node("h")).addkid(Node("c").addkid(
Node("l")))).addkid(Node("e")))
B = (Node("f").addkid(
Node("a").addkid(Node("d")).addkid(Node("r").addkid(
Node("b")))).addkid(Node("e")))
dist = compare.distance(A, B)
print dist
assert dist == 3
def test_distance(self):
trees = itertools.product([tree1(), tree2(), tree3(), tree4()], repeat=2)
for a,b in trees:
ab = compare.distance(a,b)
ba = compare.distance(b,a)
#print '-----------------------------'
#print a
#print '------'
#print b
#print '------'
#print ab, ba
self.assertEquals(ab,ba)
self.assertTrue((ab == 0 and a is b) or a is not b)
#break
trees = itertools.product([tree1(), tree2(), tree3(), tree4()], repeat=3)
for a,b,c in trees:
ab = compare.distance(a,b)
bc = compare.distance(b,c)
ac = compare.distance(a,c)
self.assertTrue(ac <= ab + bc)
def test_paper_tree():
A = (Node("f").addkid(
Node("d").addkid(Node("a")).addkid(Node("c").addkid(
Node("b")))).addkid(Node("e")))
B = (Node("f").addkid(
Node("c").addkid(Node("d").addkid(Node("a")).addkid(
Node("b")))).addkid(Node("e")))
#print A
#print
#print B
dist = compare.distance(A, B)
assert dist == 2
def test_distance(self):
trees = itertools.product(
[tree1(), tree2(), tree3(), tree4()], repeat=2)
for a, b in trees:
ab = compare.distance(a, b)
ba = compare.distance(b, a)
#print '-----------------------------'
#print a
#print '------'
#print b
#print '------'
#print ab, ba
self.assertEquals(ab, ba)
self.assertTrue((ab == 0 and a is b) or a is not b)
#break
trees = itertools.product(
[tree1(), tree2(), tree3(), tree4()], repeat=3)
for a, b, c in trees:
ab = compare.distance(a, b)
bc = compare.distance(b, c)
ac = compare.distance(a, c)
self.assertTrue(ac <= ab + bc)
def test_simplelabelchange(self):
A = (
Node("f")
.addkid(Node("a")
.addkid(Node("h"))
.addkid(Node("c")
.addkid(Node("l"))))
.addkid(Node("e"))
)
B = (
Node("f")
.addkid(Node("a")
.addkid(Node("d"))
.addkid(Node("c")
.addkid(Node("b"))))
.addkid(Node("e"))
)
assert compare.distance(A, B) == 2
def test_incorrect_behavior_regression():
A = (
Node("a")
.addkid(Node("b")
.addkid(Node("x"))
.addkid(Node("y"))
)
)
B = (
Node("a")
.addkid(Node("x"))
.addkid(Node("b")
.addkid(Node("y"))
)
)
dist = compare.distance(A, B)
print dist
assert dist == 2
def test_simplelabelchange():
A = (
Node("f")
.addkid(Node("a")
.addkid(Node("h"))
.addkid(Node("c")
.addkid(Node("l"))))
.addkid(Node("e"))
)
B = (
Node("f")
.addkid(Node("a")
.addkid(Node("d"))
.addkid(Node("r")
.addkid(Node("b"))))
.addkid(Node("e"))
)
dist = compare.distance(A,B)
print dist
assert dist == 3
trial_results = np.zeros(TRIALS, dtype=np.float64)
trial_distances = np.zeros(TRIALS, dtype=np.float64)
print "Trial",
for trial in xrange(TRIALS):
log.write("\tTrial: %d\n" % trial)
original = Move.generate_random_move()
mutant = original.copy()
for x in xrange(radiation):
mutant = mutant.mutate()
prior_time = time.time()
results = np.zeros(10 ** 6, dtype=np.float64)
for i, row in enumerate(itertools.product(xrange(0, 100, 10), repeat=6)):
s = state(row)
results[i] = original.evaluate(s) - mutant.evaluate(s)
# print sp.pearsonr(original_values, mutant_values)
d = distance(original, mutant)
deviation = np.std(results, dtype=np.float64)
log.write("\t\tOriginal: %s\n" % (original,))
log.write("\t\tMutant: %s\n" % (mutant,))
log.write("\t\tDistance: %d\n" % (d,))
log.write("\t\tNumerical Deviation %f\n" % (deviation,))
log.write("\t\tTime: %f\n" % round(time.time() - prior_time, 2))
distance_to_numerical_log.write("%d, %f\n" % (d, deviation))
trial_distances[trial] = d
trial_results[trial] = deviation
print trial,
print ""
log.write("\tDistance Mean: %f\n" % np.mean(trial_distances))
log.write("\tDistance Stdev: %f\n" % np.std(trial_distances))
log.write("\tDeviation Mean: %f\n" % np.mean(trial_results))
log.write("\tDeviation Stdev: %f\n" % np.std(trial_results))
def test_empty_tree_distance():
assert compare.distance(Node(''), Node('')) == 0
assert compare.distance(Node('a'), Node('')) == 1
assert compare.distance(Node(''), Node('b')) == 1
def test_empty_tree_distance():
assert compare.distance(Node(''), Node('')) == 0
assert compare.distance(Node('a'), Node('')) == 1
assert compare.distance(Node(''), Node('b')) == 1
def test_incorrect_behavior_regression():
A = (Node("a").addkid(Node("b").addkid(Node("x")).addkid(Node("y"))))
B = (Node("a").addkid(Node("x")).addkid(Node("b").addkid(Node("y"))))
dist = compare.distance(A, B)
print dist
assert dist == 2
def delta_conll(a, b):
return float(distance(a.tokens[0], b.tokens[0],
get_label=lambda t: t.deprel or "",
get_children=lambda t: t.children,
label_dist=strdist))
def delta_tree(a, b):
return float(distance(a, b,
get_label=lambda t: t.node,
get_children=lambda t: t,
label_dist=strdist))
