def test_similarity_1(track_abundance):
a = MinHash(20, 10, track_abundance=track_abundance)
b = MinHash(20, 10, track_abundance=track_abundance)
a.add_sequence('TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA')
b.add_sequence('TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA')
assert round(a.similarity(b), 3) == 1.0
assert round(b.similarity(b), 3) == 1.0
assert round(b.similarity(a), 3) == 1.0
assert round(a.similarity(a), 3) == 1.0
# add same sequence again
b.add_sequence('TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA')
assert round(a.similarity(b), 3) == 1.0
assert round(b.similarity(b), 3) == 1.0
assert round(b.similarity(a), 3) == 1.0
assert round(a.similarity(a), 3) == 1.0
b.add_sequence('GATTGGTGCACACTTAACTGGGTGCCGCGCTGGTGCTGATCCATGAAGTT')
x = a.similarity(b)
assert x >= 0.3, x
x = b.similarity(a)
assert x >= 0.3, x
assert round(a.similarity(a), 3) == 1.0
assert round(b.similarity(b), 3) == 1.0
def test_mh_similarity_downsample_true(track_abundance):
# verify sim(a, b) == sim(b, a), with and without ignore_abundance
# max_hash = 50
a = MinHash(0, 20, max_hash=50, track_abundance=track_abundance)
# max_hash = 100
b = MinHash(0, 20, max_hash=100, track_abundance=track_abundance)
a_values = {1: 5, 3: 3, 5: 2, 8: 2}
b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1}
if track_abundance:
a.set_abundances(a_values)
b.set_abundances(b_values)
else:
a.add_many(a_values.keys())
b.add_many(b_values.keys())
# downsample=True => no error; values should match either way
x = a.similarity(b, ignore_abundance=True, downsample=True)
y = b.similarity(a, ignore_abundance=True, downsample=True)
assert x == y
# downsample=True => no error; values should match either way
x = a.similarity(b, ignore_abundance=False, downsample=True)
y = b.similarity(a, ignore_abundance=False, downsample=True)
assert x == y
def test_mh_angular_similarity_2():
# check actual angular similarity for a second non-trivial case
a = MinHash(0, 20, max_hash=100, track_abundance=True)
b = MinHash(0, 20, max_hash=100, track_abundance=True)
a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2, 70: 70})
b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1, 70: 70})
assert round(a.similarity(b), 4) == 0.9728
# ignore_abundance => jaccard
assert a.similarity(b, ignore_abundance=True) == 5. / 7.
def test_consume_lowercase(track_abundance):
a = MinHash(20, 10, track_abundance=track_abundance)
b = MinHash(20, 10, track_abundance=track_abundance)
a.add_sequence('TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA'.lower())
b.add_sequence('TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA')
assert round(a.similarity(b), 3) == 1.0
assert round(b.similarity(b), 3) == 1.0
assert round(b.similarity(a), 3) == 1.0
assert round(a.similarity(a), 3) == 1.0
def test_mh_similarity_downsample_errors(track_abundance):
# test downsample=False (default) argument to MinHash.similarity
# max_hash = 50
a = MinHash(0, 20, max_hash=50, track_abundance=track_abundance)
# max_hash = 100
b = MinHash(0, 20, max_hash=100, track_abundance=track_abundance)
a_values = {1: 5, 3: 3, 5: 2, 8: 2}
b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1}
if track_abundance:
a.set_abundances(a_values)
b.set_abundances(b_values)
else:
a.add_many(a_values.keys())
b.add_many(b_values.keys())
# error, incompatible max hash
with pytest.raises(ValueError) as e:
a.similarity(b, ignore_abundance=True) # downsample=False
assert 'mismatch in scaled; comparison fail' in str(e.value)
with pytest.raises(ValueError) as e:
a.similarity(b, ignore_abundance=False) # downsample=False
assert 'mismatch in scaled; comparison fail' in str(e.value)
with pytest.raises(ValueError) as e:
b.similarity(a, ignore_abundance=True) # downsample=False
assert 'mismatch in scaled; comparison fail' in str(e.value)
with pytest.raises(ValueError) as e:
b.similarity(a, ignore_abundance=False) # downsample=false
assert 'mismatch in scaled; comparison fail' in str(e.value)
def test_div_zero(track_abundance):
# verify that empty MHs do not yield divide by zero errors for similarity
mh = MinHash(1, 4, track_abundance=track_abundance)
mh2 = mh.copy_and_clear()
mh.add_sequence('ATGC')
assert mh.similarity(mh2) == 0
assert mh2.similarity(mh) == 0
def test_minhash_abund_merge_flat():
# this targets a segfault caused by trying to compute similarity
# of a signature with abundance and a signature without abundance.
# the correct behavior for now is to calculate simple Jaccard,
# i.e. 'flatten' both of them.
a = MinHash(0, 10, track_abundance=True, max_hash=5000)
b = MinHash(0, 10, max_hash=5000)
for i in range(0, 10, 2):
a.add_hash(i)
for j in range(0, 10, 3):
b.add_hash(i)
# these crashed, previously.
assert a.similarity(b) == 0.2
assert b.similarity(a) == 0.2
def test_div_zero(track_abundance):
# verify that empty MHs do not yield divide by zero errors for similarity
mh = MinHash(1, 4, track_abundance=track_abundance)
mh2 = mh.copy_and_clear()
mh.add_sequence('ATGC')
assert mh.similarity(mh2) == 0
assert mh2.similarity(mh) == 0
def test_minhash_abund_merge_flat():
# this targets a segfault caused by trying to compute similarity
# of a signature with abundance and a signature without abundance.
# the correct behavior for now is to calculate simple Jaccard,
# i.e. 'flatten' both of them.
a = MinHash(0, 10, track_abundance=True, max_hash=5000)
b = MinHash(0, 10, max_hash=5000)
for i in range(0, 10, 2):
a.add_hash(i)
for j in range(0, 10, 3):
b.add_hash(i)
# these crashed, previously.
assert a.similarity(b) == 0.2
assert b.similarity(a) == 0.2
def test_mh_jaccard_similarity():
# check actual Jaccard value for a non-trivial case
a = MinHash(0, 20, max_hash=50, track_abundance=False)
b = MinHash(0, 20, max_hash=50, track_abundance=False)
a.add_many([1, 3, 5, 8])
b.add_many([1, 3, 5, 6, 8, 10])
assert a.similarity(b) == 4. / 6.
def test_mh_similarity_downsample_angular_value():
# test downsample=True argument to MinHash.similarity
# max_hash = 50
a = MinHash(0, 20, max_hash=50, track_abundance=True)
# max_hash = 100
b = MinHash(0, 20, max_hash=100, track_abundance=True)
a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2, 70: 70})
b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1, 70: 70})
# the hash=70 will be truncated by downsampling
sim = a.similarity(b, downsample=True)
assert round(sim, 4) == 0.7703
# with ignore_abundance, will be equal to jaccard
jaccard = a.similarity(b, downsample=True, ignore_abundance=True)
assert jaccard == 4. / 6.
def test_mh_similarity_downsample_jaccard_value():
# check jaccard value after downsampling
# max_hash = 50
a = MinHash(0, 20, max_hash=50, track_abundance=False)
# max_hash = 100
b = MinHash(0, 20, max_hash=100, track_abundance=False)
a.add_many([1, 3, 5, 8, 70])
b.add_many([1, 3, 5, 6, 8, 10, 70])
# the hash=70 will be truncated by downsampling
assert a.similarity(b, downsample=True) == 4. / 6.
def test_mh_angular_similarity():
# check actual angular similarity for a non-trivial case, taken from:
# https://www.sciencedirect.com/topics/computer-science/cosine-similarity
# note: angular similarity is 1 - 2*(acos(sim) / pi), when elements
# are always positive (https://en.wikipedia.org/wiki/Cosine_similarity)
a = MinHash(0, 20, max_hash=50, track_abundance=True)
b = MinHash(0, 20, max_hash=50, track_abundance=True)
a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2})
b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1})
cos_sim = 0.9356
angular_sim = 1 - 2 * math.acos(cos_sim) / math.pi
assert round(angular_sim, 4) == 0.7703
assert round(a.similarity(b), 4) == round(angular_sim, 4)
def test_mh_similarity_diff_protein(track_abundance):
a = MinHash(20, 5, False, track_abundance=track_abundance)
b = MinHash(20, 5, True, track_abundance=track_abundance)
with pytest.raises(ValueError):
a.similarity(b)
def test_mh_similarity_diff_ksize(track_abundance):
a = MinHash(20, 5, track_abundance=track_abundance)
b = MinHash(20, 6, track_abundance=track_abundance)
with pytest.raises(ValueError):
a.similarity(b)
def test_mh_similarity_diff_seed(track_abundance):
a = MinHash(20, 5, track_abundance=track_abundance, seed=1)
b = MinHash(20, 5, track_abundance=track_abundance, seed=2)
with pytest.raises(ValueError):
a.similarity(b)
def test_mh_similarity_diff_max_hash(track_abundance):
a = MinHash(0, 5, track_abundance=track_abundance, max_hash=5)
b = MinHash(0, 5, track_abundance=track_abundance, max_hash=10)
with pytest.raises(ValueError):
a.similarity(b)
