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_set_abundance():
a = MinHash(20, 10, track_abundance=False)
with pytest.raises(RuntimeError) as e:
a.set_abundances({1: 3, 2: 4})
assert "track_abundance=True when constructing" in e.value.args[0]
def test_set_abundance():
a = MinHash(20, 10, track_abundance=False)
with pytest.raises(RuntimeError) as e:
a.set_abundances({1: 3, 2: 4})
assert "track_abundance=True when constructing" in e.value.args[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_set_abundances_huge():
max_hash = 4000000
a = MinHash(0, 10, track_abundance=True, max_hash=max_hash)
hashes = list(range(max_hash))
abundances = itertools.repeat(2)
a.set_abundances(dict(zip(hashes, abundances)))
def test_set_abundance_clear_3():
a = MinHash(20, 5, False, track_abundance=True)
a.add_hash(10)
assert a.get_mins(with_abundance=True) == {10: 1}
a.set_abundances({20: 1, 30: 4}, clear=False)
assert a.get_mins(with_abundance=True) == {10: 1, 20: 1, 30: 4}
def test_set_abundance_clear_2():
# default should be clear=True
a = MinHash(20, 5, False, track_abundance=True)
a.add_hash(10)
assert a.get_mins(with_abundance=True) == {10: 1}
a.set_abundances({20: 2})
assert a.get_mins(with_abundance=True) == {20: 2}
def test_set_abundance_clear():
# on empty minhash, clear should have no effect
a = MinHash(20, 5, False, track_abundance=True)
b = MinHash(20, 5, False, track_abundance=True)
a.set_abundances({1: 3, 2: 4}, clear=True)
b.set_abundances({1: 3, 2: 4}, clear=False)
assert a.get_mins() == b.get_mins()
def test_set_abundance_clear_4():
# setting the abundance of an already set hash should add
# the abundances together
a = MinHash(20, 5, False, track_abundance=True)
a.set_abundances({20: 2, 10: 1}, clear=False) # should also sort the hashes
assert a.get_mins(with_abundance=True) == {10: 1, 20: 2}
a.set_abundances({20: 1, 10: 2}, clear=False)
assert a.get_mins(with_abundance=True) == {10: 3, 20: 3}
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_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_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_set_abundance_num():
a = MinHash(2, 10, track_abundance=True)
a.set_abundances({1: 3, 2: 4})
assert a.get_mins(with_abundance=True) == {1: 3, 2: 4}
