def test_remove_from_index(self):
# Test that removing by UIDs does the correct thing.
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
d_set = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(DummyHashFunctor(), d_set, hash_kvs)
idx.build_index(descriptors)
# Attempt removing 1 uid.
idx.remove_from_index([3])
self.assertEqual(idx.descriptor_index._table, {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
4: descriptors[4],
})
self.assertEqual(idx.hash2uuids_kvstore._table, {
0: {0},
1: {1},
2: {2},
4: {4},
})
def test_build_index_fresh_build(self):
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs)
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for i, d in enumerate(descriptors):
d.set_vector(np.ones(1, float) * i)
index.build_index(descriptors)
# Make sure descriptors are now in attached index and in
# key-value-store.
self.assertEqual(descr_set.count(), 5)
for d in descriptors:
self.assertIn(d, descr_set)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 5)
for i in range(5):
self.assertSetEqual(hash_kvs.get(i), {i})
def test_update_index_with_hash_index(self):
# Similar test to `test_update_index_add_new_descriptors` but with a
# linear hash index.
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
linear_hi = LinearHashIndex() # simplest hash index, heap-sorts.
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs, linear_hi)
descriptors1 = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
descriptors2 = [
DescriptorMemoryElement('t', 5),
DescriptorMemoryElement('t', 6),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors1 + descriptors2:
d.set_vector(np.ones(1, float) * d.uuid())
# Build initial index.
index.build_index(descriptors1)
# Initial hash index should only encode hashes for first batch of
# descriptors.
self.assertSetEqual(linear_hi.index, {0, 1, 2, 3, 4})
# Update index and check that components have new data.
index.update_index(descriptors2)
# Now the hash index should include all descriptor hashes.
self.assertSetEqual(linear_hi.index, {0, 1, 2, 3, 4, 5, 6})
def test_remove_from_index(self):
# Test that removing by UIDs does the correct thing.
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
d_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(DummyHashFunctor(), d_set, hash_kvs)
idx.build_index(descriptors)
# Attempt removing 1 uid.
idx.remove_from_index([3])
self.assertEqual(
idx.descriptor_set._table, {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
4: descriptors[4],
})
self.assertEqual(idx.hash2uuids_kvstore._table, {
0: {0},
1: {1},
2: {2},
4: {4},
})
def test_update_index_with_hash_index(self):
# Similar test to `test_update_index_add_new_descriptors` but with a
# linear hash index.
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
linear_hi = LinearHashIndex() # simplest hash index, heap-sorts.
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs, linear_hi)
descriptors1 = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
descriptors2 = [
DescriptorMemoryElement('t', 5),
DescriptorMemoryElement('t', 6),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors1 + descriptors2:
d.set_vector(np.ones(1, float) * d.uuid())
# Build initial index.
index.build_index(descriptors1)
# Initial hash index should only encode hashes for first batch of
# descriptors.
self.assertSetEqual(linear_hi.index, {0, 1, 2, 3, 4})
# Update index and check that components have new data.
index.update_index(descriptors2)
# Now the hash index should include all descriptor hashes.
self.assertSetEqual(linear_hi.index, {0, 1, 2, 3, 4, 5, 6})
def test_build_index_fresh_build(self):
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs)
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for i, d in enumerate(descriptors):
d.set_vector(np.ones(1, float) * i)
index.build_index(descriptors)
# Make sure descriptors are now in attached index and in key-value-store
self.assertEqual(descr_index.count(), 5)
for d in descriptors:
self.assertIn(d, descr_index)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 5)
for i in range(5):
self.assertSetEqual(hash_kvs.get(i), {i})
def _known_ordered_euclidean(self, hash_ftor, hash_idx,
ftor_train_hook=lambda d: None):
# make vectors to return in a known euclidean distance order
i = 1000
test_descriptors = []
for j in range(i):
d = DescriptorMemoryElement('ordered', j)
d.set_vector(np.array([j, j*2], float))
test_descriptors.append(d)
random.shuffle(test_descriptors)
ftor_train_hook(test_descriptors)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor, di, kvstore,
hash_index=hash_idx,
distance_method='euclidean')
index.build_index(test_descriptors)
# Since descriptors were built in increasing distance from (0,0),
# returned descriptors for a query of [0,0] should be in index order.
q = DescriptorMemoryElement('query', i)
q.set_vector(np.array([0, 0], float))
# top result should have UUID == 0 (nearest to query)
r, dists = index.nn(q, 5)
self.assertEqual(r[0].uuid(), 0)
self.assertEqual(r[1].uuid(), 1)
self.assertEqual(r[2].uuid(), 2)
self.assertEqual(r[3].uuid(), 3)
self.assertEqual(r[4].uuid(), 4)
# global search should be in complete order
r, dists = index.nn(q, i)
for j, d, dist in zip(range(i), r, dists):
self.assertEqual(d.uuid(), j)
def _known_ordered_euclidean(self, hash_ftor, hash_idx,
ftor_train_hook=lambda d: None):
# make vectors to return in a known euclidean distance order
i = 1000
test_descriptors = []
for j in range(i):
d = DescriptorMemoryElement('ordered', j)
d.set_vector(np.array([j, j*2], float))
test_descriptors.append(d)
random.shuffle(test_descriptors)
ftor_train_hook(test_descriptors)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor, di, kvstore,
hash_index=hash_idx,
distance_method='euclidean')
index.build_index(test_descriptors)
# Since descriptors were built in increasing distance from (0,0),
# returned descriptors for a query of [0,0] should be in index order.
q = DescriptorMemoryElement('query', i)
q.set_vector(np.array([0, 0], float))
# top result should have UUID == 0 (nearest to query)
r, dists = index.nn(q, 5)
self.assertEqual(r[0].uuid(), 0)
self.assertEqual(r[1].uuid(), 1)
self.assertEqual(r[2].uuid(), 2)
self.assertEqual(r[3].uuid(), 3)
self.assertEqual(r[4].uuid(), 4)
# global search should be in complete order
r, dists = index.nn(q, i)
for j, d, dist in zip(range(i), r, dists):
self.assertEqual(d.uuid(), j)
def _random_euclidean(self,
hash_ftor,
hash_idx,
ftor_train_hook=lambda d: None):
# :param hash_ftor: Hash function class for generating hash codes for
# descriptors.
# :param hash_idx: Hash index instance to use in local LSH algo
# instance.
# :param ftor_train_hook: Function for training functor if necessary.
# make random descriptors
i = 1000
dim = 256
td = []
numpy.random.seed(self.RANDOM_SEED)
for j in range(i):
d = DescriptorMemoryElement('random', j)
d.set_vector(numpy.random.rand(dim))
td.append(d)
ftor_train_hook(td)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor,
di,
kvstore,
hash_index=hash_idx,
distance_method='euclidean')
index.build_index(td)
# test query from build set -- should return same descriptor when k=1
q = td[255]
r, dists = index.nn(q, 1)
ntools.assert_equal(r[0], q)
# test query very near a build vector
td_q = td[0]
q = DescriptorMemoryElement('query', i)
v = td_q.vector().copy()
v_min = max(v.min(), 0.1)
v[0] += v_min
v[dim - 1] -= v_min
q.set_vector(v)
r, dists = index.nn(q, 1)
ntools.assert_false(numpy.array_equal(q.vector(), td_q.vector()))
ntools.assert_equal(r[0], td_q)
# random query
q = DescriptorMemoryElement('query', i + 1)
q.set_vector(numpy.random.rand(dim))
# for any query of size k, results should at least be in distance order
r, dists = index.nn(q, 10)
for j in range(1, len(dists)):
ntools.assert_greater(dists[j], dists[j - 1])
r, dists = index.nn(q, i)
for j in range(1, len(dists)):
ntools.assert_greater(dists[j], dists[j - 1])
def test_remove_from_index_invalid_uid(self):
# Test that attempting to remove a single invalid UID causes a key
# error and does not affect index.
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
# uid -> descriptor
expected_dset_table = {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
3: descriptors[3],
4: descriptors[4],
}
# hash int -> set[uid]
expected_kvs_table = {
0: {0},
1: {1},
2: {2},
3: {3},
4: {4},
}
d_set = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(DummyHashFunctor(), d_set, hash_kvs)
idx.build_index(descriptors)
# Assert we have the correct expected values
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
# Attempt to remove descriptor with a UID we did not build with.
self.assertRaisesRegexp(
KeyError, '5',
idx.remove_from_index, [5]
)
# Index should not have been modified.
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
# Attempt to remove multiple UIDs, one valid and one invalid
self.assertRaisesRegexp(
KeyError, '5',
idx.remove_from_index, [2, 5]
)
# Index should not have been modified.
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
def test_remove_from_index_invalid_uid(self):
# Test that attempting to remove a single invalid UID causes a key
# error and does not affect index.
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
# uid -> descriptor
expected_dset_table = {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
3: descriptors[3],
4: descriptors[4],
}
# hash int -> set[uid]
expected_kvs_table = {
0: {0},
1: {1},
2: {2},
3: {3},
4: {4},
}
d_set = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(DummyHashFunctor(), d_set, hash_kvs)
idx.build_index(descriptors)
# Assert we have the correct expected values
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
# Attempt to remove descriptor with a UID we did not build with.
self.assertRaisesRegexp(
KeyError, '5',
idx.remove_from_index, [5]
)
# Index should not have been modified.
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
# Attempt to remove multiple UIDs, one valid and one invalid
self.assertRaisesRegexp(
KeyError, '5',
idx.remove_from_index, [2, 5]
)
# Index should not have been modified.
self.assertEqual(idx.descriptor_index._table, expected_dset_table)
self.assertEqual(idx.hash2uuids_kvstore._table, expected_kvs_table)
def _random_euclidean(self, hash_ftor, hash_idx,
ftor_train_hook=lambda d: None):
# :param hash_ftor: Hash function class for generating hash codes for
# descriptors.
# :param hash_idx: Hash index instance to use in local LSH algo
# instance.
# :param ftor_train_hook: Function for training functor if necessary.
# make random descriptors
i = 1000
dim = 256
td = []
np.random.seed(self.RANDOM_SEED)
for j in range(i):
d = DescriptorMemoryElement('random', j)
d.set_vector(np.random.rand(dim))
td.append(d)
ftor_train_hook(td)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor, di, kvstore,
hash_index=hash_idx,
distance_method='euclidean')
index.build_index(td)
# test query from build set -- should return same descriptor when k=1
q = td[255]
r, dists = index.nn(q, 1)
self.assertEqual(r[0], q)
# test query very near a build vector
td_q = td[0]
q = DescriptorMemoryElement('query', i)
v = td_q.vector().copy()
v_min = max(v.min(), 0.1)
v[0] += v_min
v[dim-1] -= v_min
q.set_vector(v)
r, dists = index.nn(q, 1)
self.assertFalse(np.array_equal(q.vector(), td_q.vector()))
self.assertEqual(r[0], td_q)
# random query
q = DescriptorMemoryElement('query', i+1)
q.set_vector(np.random.rand(dim))
# for any query of size k, results should at least be in distance order
r, dists = index.nn(q, 10)
for j in range(1, len(dists)):
self.assertGreater(dists[j], dists[j-1])
r, dists = index.nn(q, i)
for j in range(1, len(dists)):
self.assertGreater(dists[j], dists[j-1])
def test_remove_from_index_shared_hashes(self):
"""
Test that removing a descriptor (by UID) that shares a hash with other
descriptors does not trigger removal of its hash.
"""
# Simulate descriptors all hashing to the same hash value: 0
hash_func = DummyHashFunctor()
hash_func.get_hash = mock.Mock(return_value=np.asarray([0], bool))
d_set = MemoryDescriptorSet()
hash2uids_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(hash_func, d_set, hash2uids_kvs)
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
idx.build_index(descriptors)
# We expect the descriptor-set and kvs to look like the following now:
self.assertDictEqual(
d_set._table, {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
3: descriptors[3],
4: descriptors[4],
})
self.assertDictEqual(hash2uids_kvs._table, {0: {0, 1, 2, 3, 4}})
# Mock out hash index as if we had an implementation so we can check
# call to its remove_from_index method.
idx.hash_index = mock.Mock(spec=HashIndex)
idx.remove_from_index([2, 4])
# Only uid 2 and 4 descriptors should be gone from d-set, kvs should
# still have the 0 key and its set value should only contain uids 0, 1
# and 3. `hash_index.remove_from_index` should not be called because
# no hashes should be marked for removal.
self.assertDictEqual(d_set._table, {
0: descriptors[0],
1: descriptors[1],
3: descriptors[3],
})
self.assertDictEqual(hash2uids_kvs._table, {0: {0, 1, 3}})
idx.hash_index.remove_from_index.assert_not_called()
def test_remove_from_index_shared_hashes(self):
"""
Test that removing a descriptor (by UID) that shares a hash with other
descriptors does not trigger removal of its hash.
"""
# Simulate descriptors all hashing to the same hash value: 0
hash_func = DummyHashFunctor()
hash_func.get_hash = mock.Mock(return_value=np.asarray([0], bool))
d_set = MemoryDescriptorIndex()
hash2uids_kvs = MemoryKeyValueStore()
idx = LSHNearestNeighborIndex(hash_func, d_set, hash2uids_kvs)
# Descriptors are 1 dim, value == index.
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors:
d.set_vector(np.ones(1, float) * d.uuid())
idx.build_index(descriptors)
# We expect the descriptor-set and kvs to look like the following now:
self.assertDictEqual(d_set._table, {
0: descriptors[0],
1: descriptors[1],
2: descriptors[2],
3: descriptors[3],
4: descriptors[4],
})
self.assertDictEqual(hash2uids_kvs._table, {0: {0, 1, 2, 3, 4}})
# Mock out hash index as if we had an implementation so we can check
# call to its remove_from_index method.
idx.hash_index = mock.Mock(spec=HashIndex)
idx.remove_from_index([2, 4])
# Only uid 2 and 4 descriptors should be gone from d-set, kvs should
# still have the 0 key and its set value should only contain uids 0, 1
# and 3. `hash_index.remove_from_index` should not be called because
# no hashes should be marked for removal.
self.assertDictEqual(d_set._table, {
0: descriptors[0],
1: descriptors[1],
3: descriptors[3],
})
self.assertDictEqual(hash2uids_kvs._table, {0: {0, 1, 3}})
idx.hash_index.remove_from_index.assert_not_called()
def _random_euclidean(self, hash_ftor, hash_idx, ftor_train_hook=lambda d: None):
# make random descriptors
i = 1000
dim = 256
td = []
numpy.random.seed(self.RANDOM_SEED)
for j in xrange(i):
d = DescriptorMemoryElement("random", j)
d.set_vector(numpy.random.rand(dim))
td.append(d)
ftor_train_hook(td)
di = MemoryDescriptorIndex()
index = LSHNearestNeighborIndex(hash_ftor, di, hash_idx, distance_method="euclidean")
index.build_index(td)
# test query from build set -- should return same descriptor when k=1
q = td[255]
r, dists = index.nn(q, 1)
ntools.assert_equal(r[0], q)
# test query very near a build vector
td_q = td[0]
q = DescriptorMemoryElement("query", i)
v = td_q.vector().copy()
v_min = max(v.min(), 0.1)
v[0] += v_min
v[dim - 1] -= v_min
q.set_vector(v)
r, dists = index.nn(q, 1)
ntools.assert_false(numpy.array_equal(q.vector(), td_q.vector()))
ntools.assert_equal(r[0], td_q)
# random query
q = DescriptorMemoryElement("query", i + 1)
q.set_vector(numpy.random.rand(dim))
# for any query of size k, results should at least be in distance order
r, dists = index.nn(q, 10)
for j in xrange(1, len(dists)):
ntools.assert_greater(dists[j], dists[j - 1])
r, dists = index.nn(q, i)
for j in xrange(1, len(dists)):
ntools.assert_greater(dists[j], dists[j - 1])
DescriptorMemoryElement.MEMORY_CACHE = {}
def _known_unit(self,
hash_ftor,
hash_idx,
dist_method,
ftor_train_hook=lambda d: None):
###
# Unit vectors - Equal distance
#
dim = 5
test_descriptors = []
for i in range(dim):
v = numpy.zeros(dim, float)
v[i] = 1.
d = DescriptorMemoryElement('unit', i)
d.set_vector(v)
test_descriptors.append(d)
ftor_train_hook(test_descriptors)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor,
di,
kvstore,
hash_index=hash_idx,
distance_method=dist_method)
index.build_index(test_descriptors)
# query with zero vector
# -> all modeled descriptors have no intersection, dists should be 1.0,
# or maximum distance by histogram intersection
q = DescriptorMemoryElement('query', 0)
q.set_vector(numpy.zeros(dim, float))
r, dists = index.nn(q, dim)
# All dists should be 1.0, r order doesn't matter
for d in dists:
ntools.assert_equal(d, 1.)
# query with index element
q = test_descriptors[3]
r, dists = index.nn(q, 1)
ntools.assert_equal(r[0], q)
ntools.assert_equal(dists[0], 0.)
r, dists = index.nn(q, dim)
ntools.assert_equal(r[0], q)
ntools.assert_equal(dists[0], 0.)
def test_update_index_add_new_descriptors(self):
# Test that calling update index after a build index causes index
# components to be properly updated.
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs)
descriptors1 = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
descriptors2 = [
DescriptorMemoryElement('t', 5),
DescriptorMemoryElement('t', 6),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors1 + descriptors2:
d.set_vector(np.ones(1, float) * d.uuid())
# Build initial index.
index.build_index(descriptors1)
self.assertEqual(descr_index.count(), 5)
for d in descriptors1:
self.assertIn(d, descr_index)
for d in descriptors2:
self.assertNotIn(d, descr_index)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 5)
for i in range(5):
self.assertSetEqual(hash_kvs.get(i), {i})
# Update index and check that components have new data.
index.update_index(descriptors2)
self.assertEqual(descr_index.count(), 7)
for d in descriptors1 + descriptors2:
self.assertIn(d, descr_index)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 7)
for i in range(7):
self.assertSetEqual(hash_kvs.get(i), {i})
def test_update_index_similar_descriptors(self):
"""
Test that updating a built index with similar descriptors (same
vectors, different UUIDs) results in contained structures having an
expected state.
"""
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs)
# Similar Descriptors to build and update on (different instances)
descriptors1 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
descriptors2 = [
DescriptorMemoryElement('t', 5).set_vector([0]),
DescriptorMemoryElement('t', 6).set_vector([1]),
DescriptorMemoryElement('t', 7).set_vector([2]),
DescriptorMemoryElement('t', 8).set_vector([3]),
DescriptorMemoryElement('t', 9).set_vector([4]),
]
index.build_index(descriptors1)
index.update_index(descriptors2)
assert descr_index.count() == 10
# Above descriptors should be considered "in" the descriptor set now.
for d in descriptors1:
assert d in descr_index
for d in descriptors2:
assert d in descr_index
# Known hashes of the above descriptors should be in the KVS
assert set(hash_kvs.keys()) == {0, 1, 2, 3, 4}
assert hash_kvs.get(0) == {0, 5}
assert hash_kvs.get(1) == {1, 6}
assert hash_kvs.get(2) == {2, 7}
assert hash_kvs.get(3) == {3, 8}
assert hash_kvs.get(4) == {4, 9}
def test_update_index_similar_descriptors(self):
"""
Test that updating a built index with similar descriptors (same
vectors, different UUIDs) results in contained structures having an
expected state.
"""
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs)
# Similar Descriptors to build and update on (different instances)
descriptors1 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
descriptors2 = [
DescriptorMemoryElement('t', 5).set_vector([0]),
DescriptorMemoryElement('t', 6).set_vector([1]),
DescriptorMemoryElement('t', 7).set_vector([2]),
DescriptorMemoryElement('t', 8).set_vector([3]),
DescriptorMemoryElement('t', 9).set_vector([4]),
]
index.build_index(descriptors1)
index.update_index(descriptors2)
assert descr_set.count() == 10
# Above descriptors should be considered "in" the descriptor set now.
for d in descriptors1:
assert d in descr_set
for d in descriptors2:
assert d in descr_set
# Known hashes of the above descriptors should be in the KVS
assert set(hash_kvs.keys()) == {0, 1, 2, 3, 4}
assert hash_kvs.get(0) == {0, 5}
assert hash_kvs.get(1) == {1, 6}
assert hash_kvs.get(2) == {2, 7}
assert hash_kvs.get(3) == {3, 8}
assert hash_kvs.get(4) == {4, 9}
def test_update_index_add_new_descriptors(self):
# Test that calling update index after a build index causes index
# components to be properly updated.
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs)
descriptors1 = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
descriptors2 = [
DescriptorMemoryElement('t', 5),
DescriptorMemoryElement('t', 6),
]
# Vectors of length 1 for easy dummy hashing prediction.
for d in descriptors1 + descriptors2:
d.set_vector(np.ones(1, float) * d.uuid())
# Build initial index.
index.build_index(descriptors1)
self.assertEqual(descr_set.count(), 5)
for d in descriptors1:
self.assertIn(d, descr_set)
for d in descriptors2:
self.assertNotIn(d, descr_set)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 5)
for i in range(5):
self.assertSetEqual(hash_kvs.get(i), {i})
# Update index and check that components have new data.
index.update_index(descriptors2)
self.assertEqual(descr_set.count(), 7)
for d in descriptors1 + descriptors2:
self.assertIn(d, descr_set)
# Dummy hash function bins sum of descriptor vectors.
self.assertEqual(hash_kvs.count(), 7)
for i in range(7):
self.assertSetEqual(hash_kvs.get(i), {i})
def test_update_index_duplicate_descriptors(self):
"""
Test that updating a built index with the same descriptors results in
idempotent behavior.
"""
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs)
# Identical Descriptors to build and update on (different instances)
descriptors1 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
descriptors2 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
index.build_index(descriptors1)
index.update_index(descriptors2)
assert descr_index.count() == 5
# Above descriptors should be considered "in" the descriptor set now.
for d in descriptors1:
assert d in descr_index
for d in descriptors2:
assert d in descr_index
# Known hashes of the above descriptors should be in the KVS
assert set(hash_kvs.keys()) == {0, 1, 2, 3, 4}
assert hash_kvs.get(0) == {0}
assert hash_kvs.get(1) == {1}
assert hash_kvs.get(2) == {2}
assert hash_kvs.get(3) == {3}
assert hash_kvs.get(4) == {4}
def test_update_index_duplicate_descriptors(self):
"""
Test that updating a built index with the same descriptors results in
idempotent behavior.
"""
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs)
# Identical Descriptors to build and update on (different instances)
descriptors1 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
descriptors2 = [
DescriptorMemoryElement('t', 0).set_vector([0]),
DescriptorMemoryElement('t', 1).set_vector([1]),
DescriptorMemoryElement('t', 2).set_vector([2]),
DescriptorMemoryElement('t', 3).set_vector([3]),
DescriptorMemoryElement('t', 4).set_vector([4]),
]
index.build_index(descriptors1)
index.update_index(descriptors2)
assert descr_set.count() == 5
# Above descriptors should be considered "in" the descriptor set now.
for d in descriptors1:
assert d in descr_set
for d in descriptors2:
assert d in descr_set
# Known hashes of the above descriptors should be in the KVS
assert set(hash_kvs.keys()) == {0, 1, 2, 3, 4}
assert hash_kvs.get(0) == {0}
assert hash_kvs.get(1) == {1}
assert hash_kvs.get(2) == {2}
assert hash_kvs.get(3) == {3}
assert hash_kvs.get(4) == {4}
def _known_unit(self, hash_ftor, hash_idx, dist_method,
ftor_train_hook=lambda d: None):
###
# Unit vectors - Equal distance
#
dim = 5
test_descriptors = []
for i in range(dim):
v = np.zeros(dim, float)
v[i] = 1.
d = DescriptorMemoryElement('unit', i)
d.set_vector(v)
test_descriptors.append(d)
ftor_train_hook(test_descriptors)
di = MemoryDescriptorIndex()
kvstore = MemoryKeyValueStore()
index = LSHNearestNeighborIndex(hash_ftor, di, kvstore,
hash_index=hash_idx,
distance_method=dist_method)
index.build_index(test_descriptors)
# query with zero vector
# -> all modeled descriptors have no intersection, dists should be 1.0,
# or maximum distance by histogram intersection
q = DescriptorMemoryElement('query', 0)
q.set_vector(np.zeros(dim, float))
r, dists = index.nn(q, dim)
# All dists should be 1.0, r order doesn't matter
for d in dists:
self.assertEqual(d, 1.)
# query with index element
q = test_descriptors[3]
r, dists = index.nn(q, 1)
self.assertEqual(r[0], q)
self.assertEqual(dists[0], 0.)
r, dists = index.nn(q, dim)
self.assertEqual(r[0], q)
self.assertEqual(dists[0], 0.)
def test_build_index_fresh_build_with_hash_index(self):
descr_index = MemoryDescriptorIndex()
hash_kvs = MemoryKeyValueStore()
linear_hi = LinearHashIndex() # simplest hash index, heap-sorts.
index = LSHNearestNeighborIndex(DummyHashFunctor(),
descr_index, hash_kvs, linear_hi)
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for i, d in enumerate(descriptors):
d.set_vector(np.ones(1, float) * i)
index.build_index(descriptors)
# Hash index should have been built with hash vectors, and linearHI
# converts those to integers for storage.
self.assertEqual(linear_hi.index, {0, 1, 2, 3, 4})
def test_build_index_fresh_build_with_hash_index(self):
descr_set = MemoryDescriptorSet()
hash_kvs = MemoryKeyValueStore()
linear_hi = LinearHashIndex() # simplest hash index, heap-sorts.
index = LSHNearestNeighborIndex(DummyHashFunctor(), descr_set,
hash_kvs, linear_hi)
descriptors = [
DescriptorMemoryElement('t', 0),
DescriptorMemoryElement('t', 1),
DescriptorMemoryElement('t', 2),
DescriptorMemoryElement('t', 3),
DescriptorMemoryElement('t', 4),
]
# Vectors of length 1 for easy dummy hashing prediction.
for i, d in enumerate(descriptors):
d.set_vector(np.ones(1, float) * i)
index.build_index(descriptors)
# Hash index should have been built with hash vectors, and linearHI
# converts those to integers for storage.
self.assertEqual(linear_hi.index, {0, 1, 2, 3, 4})
def _known_unit(self, hash_ftor, hash_idx, dist_method, ftor_train_hook=lambda d: None):
###
# Unit vectors - Equal distance
#
dim = 5
test_descriptors = []
for i in xrange(dim):
v = numpy.zeros(dim, float)
v[i] = 1.0
d = DescriptorMemoryElement("unit", i)
d.set_vector(v)
test_descriptors.append(d)
ftor_train_hook(test_descriptors)
di = MemoryDescriptorIndex()
index = LSHNearestNeighborIndex(hash_ftor, di, hash_idx, distance_method=dist_method)
index.build_index(test_descriptors)
# query with zero vector
# -> all modeled descriptors have no intersection, dists should be 1.0,
# or maximum distance by histogram intersection
q = DescriptorMemoryElement("query", 0)
q.set_vector(numpy.zeros(dim, float))
r, dists = index.nn(q, dim)
# All dists should be 1.0, r order doesn't matter
for d in dists:
ntools.assert_equal(d, 1.0)
# query with index element
q = test_descriptors[3]
r, dists = index.nn(q, 1)
ntools.assert_equal(r[0], q)
ntools.assert_equal(dists[0], 0.0)
r, dists = index.nn(q, dim)
ntools.assert_equal(r[0], q)
ntools.assert_equal(dists[0], 0.0)
DescriptorMemoryElement.MEMORY_CACHE = {}
