def test_signature_similarity():
"""The probability that two sets' signatures match at some index
are equal is equal to the Jaccard similarity between the two"""
dim = 100
n_tests = 100
expected_error = 1 / sqrt(dim) # Expected error is O(1/sqrt(dim))
mh = MinHashSignature(dim)
err = 0.0
for test in range(n_tests):
# Create random sets and their signatures
sets = (randset(), randset())
sigs = map(mh.sign, sets)
# Calculate true jaccard similarity, and sim of signatures
jsim = jaccard_sim(*sets)
ssim = sigsim(*sigs, dim=dim)
# Accumulate error
err += abs(jsim - ssim)
# Over n_tests large, we should be within upper bound of expected error.
avg_err = err / n_tests
assert expected_error >= avg_err, "Accuracy test failed. (avg error: %f)" % avg_err
def test_signature_length():
"""Signatures should have correct dimension"""
dim = 100
mh = MinHashSignature(dim)
assert dim == len(mh.sign(randset()))
def test_signature_length():
"""Signatures should have correct dimension"""
dim = 100
mh = MinHashSignature(dim)
assert dim == len(mh.sign(randset()))
def test_consistent_signature():
"""Signatures should be consistent"""
mh = MinHashSignature(100)
s = randset()
assert mh.sign(s) == mh.sign(s)
def test_consistent_signature():
"""Signatures should be consistent"""
mh = MinHashSignature(100)
s = randset()
assert mh.sign(s) == mh.sign(s)