def test_searcher():
data = pkl.load(open(relpath('./testdata/test_searcher_data.pkl')))
m = LOPQModel.load_proto(relpath('./testdata/random_test_model.lopq'))
q = np.ones(8)
# Test add_data
searcher = LOPQSearcher(m)
searcher.add_data(data)
searcher_instance_battery(searcher, q)
# Test add_codes
searcher = LOPQSearcher(m)
codes = [m.predict(x) for x in data]
searcher.add_codes(codes)
searcher_instance_battery(searcher, q)
def test_searcher():
data = pkl.load(open(relpath('./testdata/test_searcher_data.pkl')))
m = LOPQModel.load_proto(relpath('./testdata/random_test_model.lopq'))
searcher = LOPQSearcher(m)
searcher.add_data(data)
q = np.ones(8)
retrieved, visited = searcher.get_result_quota(q)
assert_equal(len(retrieved), 12)
assert_equal(visited, 3)
retrieved, visited = searcher.get_result_quota(q, quota=20)
assert_equal(len(retrieved), 28)
assert_equal(visited, 5)
def test_searcher():
data = pkl.load(open(relpath('./testdata/test_searcher_data.pkl')))
m = LOPQModel.load_proto(relpath('./testdata/random_test_model.lopq'))
q = np.ones(8)
# Test add_data
searcher = LOPQSearcher(m)
searcher.add_data(data)
searcher_instance_battery(searcher, q)
# Test add_codes
searcher = LOPQSearcher(m)
codes = [m.predict(x) for x in data]
searcher.add_codes(codes)
searcher_instance_battery(searcher, q)
def test_searcher():
data = pkl.load(open(relpath("./testdata/test_searcher_data.pkl")))
m = LOPQModel.load_proto(relpath("./testdata/random_test_model.lopq"))
searcher = LOPQSearcher(m)
searcher.add_data(data)
q = np.ones(8)
retrieved, visited = searcher.get_result_quota(q)
assert_equal(len(retrieved), 12)
assert_equal(visited, 3)
retrieved, visited = searcher.get_result_quota(q, quota=20)
assert_equal(len(retrieved), 28)
assert_equal(visited, 5)
def test_oxford5k():
random_state = 40
data = load_oxford_data()
train, test = train_test_split(data,
test_size=0.2,
random_state=random_state)
# Compute distance-sorted neighbors in training set for each point in test set
nns = compute_all_neighbors(test, train)
# Fit model
m = LOPQModel(V=16, M=8)
m.fit(train, n_init=1, random_state=random_state)
# Assert correct code computation
assert_equal(m.predict(test[0]),
((3, 2), (14, 164, 83, 49, 185, 29, 196, 250)))
# Assert low number of empty cells
h = get_cell_histogram(train, m)
assert_equal(np.count_nonzero(h == 0), 6)
# Assert true NN recall on test set
searcher = LOPQSearcher(m)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
# Test partial fitting with just coarse quantizers
m2 = LOPQModel(V=16, M=8, parameters=(m.Cs, None, None, None))
m2.fit(train, n_init=1, random_state=random_state)
searcher = LOPQSearcher(m2)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
# Test partial fitting with coarse quantizers and rotations
m3 = LOPQModel(V=16, M=8, parameters=(m.Cs, m.Rs, m.mus, None))
m3.fit(train, n_init=1, random_state=random_state)
searcher = LOPQSearcher(m3)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
def test_oxford5k():
random_state = 40
data = load_oxford_data()
train, test = train_test_split(data, test_size=0.2, random_state=random_state)
# Compute distance-sorted neighbors in training set for each point in test set
nns = compute_all_neighbors(test, train)
# Fit model
m = LOPQModel(V=16, M=8)
m.fit(train, n_init=1, random_state=random_state)
# Assert correct code computation
assert_equal(m.predict(test[0]), ((3, 2), (14, 164, 83, 49, 185, 29, 196, 250)))
# Assert low number of empty cells
h = get_cell_histogram(train, m)
assert_equal(np.count_nonzero(h == 0), 6)
# Assert true NN recall on test set
searcher = LOPQSearcher(m)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
# Test partial fitting with just coarse quantizers
m2 = LOPQModel(V=16, M=8, parameters=(m.Cs, None, None, None))
m2.fit(train, n_init=1, random_state=random_state)
searcher = LOPQSearcher(m2)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
# Test partial fitting with coarse quantizers and rotations
m3 = LOPQModel(V=16, M=8, parameters=(m.Cs, m.Rs, m.mus, None))
m3.fit(train, n_init=1, random_state=random_state)
searcher = LOPQSearcher(m3)
searcher.add_data(train)
recall, _ = get_recall(searcher, test, nns)
assert_true(np.all(recall > [0.51, 0.92, 0.97, 0.97]))
