def _run_acc(size, seed, num_perm):
m = MinHash(num_perm=num_perm)
s = set()
random.seed(seed)
for i in range(size):
v = int_bytes(random.randint(1, size))
m.digest(sha1(v))
s.add(v)
return (m, s)
def _run_acc(size, seed, num_perm):
m = MinHash(num_perm=num_perm)
s = set()
random.seed(seed)
for i in range(size):
v = int_bytes(random.randint(1, size))
m.digest(sha1(v))
s.add(v)
return (m, s)
def run_perf(card, num_perm):
m = MinHash(num_perm=num_perm)
logging.info("MinHash using %d permutation functions" % num_perm)
start = time.clock()
for i in range(card):
m.digest(sha1(int_bytes(i)))
duration = time.clock() - start
logging.info("Digested %d hashes in %.4f sec" % (card, duration))
return duration
def run_perf(card, num_perm):
m = MinHash(num_perm=num_perm)
logging.info("MinHash using %d permutation functions" % num_perm)
start = time.clock()
for i in range(card):
m.digest(sha1(int_bytes(i)))
duration = time.clock() - start
logging.info("Digested %d hashes in %.4f sec" % (card, duration))
return duration
def _run_minhash(A, B, data, seed, p):
(a_start, a_end), (b_start, b_end) = A, B
hasher = pyhash.murmur3_32()
m1 = MinHash(num_perm=2**p)
m2 = MinHash(num_perm=2**p)
for i in xrange(a_start, a_end):
m1.digest(Hash(hasher(data[i], seed=seed)))
for i in xrange(b_start, b_end):
m2.digest(Hash(hasher(data[i], seed=seed)))
return _minhash_inclusion(m1, m2)
def _run_minhash(A, B, data, seed, num_perm):
(a_start, a_end), (b_start, b_end) = A, B
hasher = pyhash.murmur3_32()
m1 = MinHash(num_perm=num_perm)
m2 = MinHash(num_perm=num_perm)
for i in xrange(a_start, a_end):
m1.digest(Hash(hasher(data[i], seed=seed)))
for i in xrange(b_start, b_end):
m2.digest(Hash(hasher(data[i], seed=seed)))
return jaccard([m1, m2])
def _run_minhash(A, B, data, seed, bs, num_perm):
(a_start, a_end), (b_start, b_end) = A, B
hasher = pyhash.murmur3_32()
m1 = MinHash(num_perm=num_perm)
m2 = MinHash(num_perm=num_perm)
for i in xrange(a_start, a_end):
m1.digest(Hash(hasher(data[i], seed=seed)))
for i in xrange(b_start, b_end):
m2.digest(Hash(hasher(data[i], seed=seed)))
return [m1.jaccard(m2)] + \
[_b_bit_minhash_jaccard(m1, m2, b) for b in bs]
def dict_to_minhash(v):
"""
Generates a Minhash for a dict object
:param v: dictionary
:return: minhash
"""
m_ = MinHash()
tokens = my_tokenizer(v)
for t in tokens:
m_.digest(sha1(t.encode('utf8')))
return m_
def test_pickle(self):
lsh = LSH(threshold=0.5, num_perm=16)
m1 = MinHash(16)
m1.digest(sha1("a".encode("utf8")))
m2 = MinHash(16)
m2.digest(sha1("b".encode("utf8")))
lsh.insert("a", m1)
lsh.insert("b", m2)
lsh2 = pickle.loads(pickle.dumps(lsh))
result = lsh.query(m1)
self.assertTrue("a" in result)
result = lsh.query(m2)
self.assertTrue("b" in result)
def eg1():
m1 = MinHash()
m2 = MinHash()
for d in data1:
m1.digest(sha1(d.encode('utf8')))
for d in data2:
m2.digest(sha1(d.encode('utf8')))
print("Estimated Jaccard for data1 and data2 is", jaccard(m1, m2))
s1 = set(data1)
s2 = set(data2)
actual_jaccard = float(len(s1.intersection(s2))) /\
float(len(s1.union(s2)))
print("Actual Jaccard for data1 and data2 is", actual_jaccard)
def eg1():
m1 = MinHash()
m2 = MinHash()
for d in data1:
m1.digest(sha1(d.encode('utf8')))
for d in data2:
m2.digest(sha1(d.encode('utf8')))
print("Estimated Jaccard for data1 and data2 is", jaccard([m1, m2]))
s1 = set(data1)
s2 = set(data2)
actual_jaccard = float(len(s1.intersection(s2))) /\
float(len(s1.union(s2)))
print("Actual Jaccard for data1 and data2 is", actual_jaccard)
def test_query(self):
lsh = LSH(threshold=0.5, num_perm=16)
m1 = MinHash(16)
m1.digest(sha1("a".encode("utf8")))
m2 = MinHash(16)
m2.digest(sha1("b".encode("utf8")))
lsh.insert("a", m1)
lsh.insert("b", m2)
result = lsh.query(m1)
self.assertTrue("a" in result)
result = lsh.query(m2)
self.assertTrue("b" in result)
m3 = MinHash(18)
self.assertRaises(ValueError, lsh.query, m3)
def test_insert(self):
lsh = LSH(threshold=0.5, num_perm=16)
m1 = MinHash(16)
m1.digest(sha1("a".encode("utf8")))
m2 = MinHash(16)
m2.digest(sha1("b".encode("utf8")))
lsh.insert("a", m1)
lsh.insert("b", m2)
for t in lsh.hashtables:
self.assertTrue(len(t) >= 1)
items = []
for H in t:
items.extend(t[H])
self.assertTrue("a" in items)
self.assertTrue("b" in items)
m3 = MinHash(18)
self.assertRaises(ValueError, lsh.insert, "c", m3)
def eg1():
m1 = MinHash()
m2 = MinHash()
m3 = MinHash()
for d in data1:
m1.digest(sha1(d.encode('utf8')))
for d in data2:
m2.digest(sha1(d.encode('utf8')))
for d in data3:
m3.digest(sha1(d.encode('utf8')))
# Create LSH index
lsh = LSH(threshold=0.5)
lsh.insert("m2", m2)
lsh.insert("m3", m3)
result = lsh.query(m1)
print("Approximate neighbours with Jaccard similarity > 0.5", result)
def _run_minhash(data, seed, p):
hasher = pyhash.murmur3_32()
m = MinHash(num_perm=2**p)
for d in data:
m.digest(Hash(hasher(d, seed=seed)))
return m.count()
