def test_output_simple():
"""
Elbow should be at 2.
"""
X = np.array([10, 9, 3, 2, 1])
elbows, _ = select_dimension(X, n_elbows=1)
assert_equal(elbows[0], 2)
def test_output_two_block_sbm():
np.random.seed(10)
n_communities = [100, 100]
P = np.array([[0.5, 0.1], [0.1, 0.5]])
A = sbm(n_communities, P)
elbows, _ = select_dimension(A, n_elbows=2)
assert_equal(elbows[0], 2)
def test_output_uniform():
"""
Generate two sets of synthetic eigenvalues based on two uniform distributions.
The elbow must be at 50.
"""
np.random.seed(9)
x1 = np.random.uniform(0, 45, 50)
x2 = np.random.uniform(55, 100, 50)
X = np.sort(np.hstack([x1, x2]))[::-1]
elbows, _ = select_dimension(X, n_elbows=1)
assert_equal(elbows[0], 50)
def test_output_synthetic():
data, l = generate_data(10, 3)
elbows, _, _ = select_dimension(X=data,
n_elbows=2,
return_likelihoods=True)
assert_equal(elbows, [2, 4])
def test_invalid_inputes():
X, D = generate_data()
# invalid n_elbows
with pytest.raises(ValueError):
bad_n_elbows = -2
select_dimension(X, n_elbows=bad_n_elbows)
with pytest.raises(ValueError):
bad_n_elbows = "string"
select_dimension(X, n_elbows=bad_n_elbows)
# invalid n_components
with pytest.raises(ValueError):
bad_n_components = -1
select_dimension(X, n_components=bad_n_components)
with pytest.raises(ValueError):
bad_n_components = "string"
select_dimension(X, n_components=bad_n_components)
# invalid threshold
with pytest.raises(ValueError):
bad_threshold = -2
select_dimension(X, threshold=bad_threshold)
with pytest.raises(ValueError):
bad_threshold = "string"
select_dimension(X, threshold=bad_threshold)
with pytest.raises(IndexError):
bad_threshold = 1000000
select_dimension(X, threshold=bad_threshold)
# invalid X
with pytest.raises(ValueError):
bad_X = -2
select_dimension(X=bad_X)
with pytest.raises(ValueError):
# input is tensor
bad_X = np.random.normal(size=(100, 10, 10))
select_dimension(X=bad_X)
with pytest.raises(ValueError):
bad_X = np.random.normal(size=100).reshape(100, -1)
select_dimension(X=bad_X)
