def test_sparse_encode_unavailable_positivity(algo):
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
err_msg = "Positive constraint not supported for '{}' coding method."
err_msg = err_msg.format(algo)
with pytest.raises(ValueError, match=err_msg):
sparse_encode(X, V, algorithm=algo, positive=True)
def test_sparse_encode_input():
n_components = 100
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
Xf = check_array(X, order='F')
for algo in ('lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'):
a = sparse_encode(X, V, algorithm=algo)
b = sparse_encode(Xf, V, algorithm=algo)
assert_array_almost_equal(a, b)
def test_sparse_encode_error():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
code = sparse_encode(X, V, alpha=0.001)
assert not np.all(code == 0)
assert np.sqrt(np.sum((np.dot(code, V) - X)**2)) < 0.1
def test_sparse_encode_shapes():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
for algo in ('lasso_lars', 'lasso_cd', 'lars', 'omp', 'threshold'):
code = sparse_encode(X, V, algorithm=algo)
assert code.shape == (n_samples, n_components)
def test_sparse_encode_positivity(algo, positive):
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
code = sparse_encode(X, V, algorithm=algo, positive=positive)
if positive:
assert (code >= 0).all()
else:
assert (code < 0).any()
def test_sparse_encode_shapes_omp():
rng = np.random.RandomState(0)
algorithms = ['omp', 'lasso_lars', 'lasso_cd', 'lars', 'threshold']
for n_components, n_samples in itertools.product([1, 5], [1, 9]):
X_ = rng.randn(n_samples, n_features)
dictionary = rng.randn(n_components, n_features)
for algorithm, n_jobs in itertools.product(algorithms, [1, 3]):
code = sparse_encode(X_,
dictionary,
algorithm=algorithm,
n_jobs=n_jobs)
assert code.shape == (n_samples, n_components)
def test_dict_learning_online_partial_fit():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V**2, axis=1)[:, np.newaxis]
dict1 = MiniBatchDictionaryLearning(n_components,
n_iter=10 * len(X),
batch_size=1,
alpha=1,
shuffle=False,
dict_init=V,
random_state=0).fit(X)
dict2 = MiniBatchDictionaryLearning(n_components,
alpha=1,
n_iter=1,
dict_init=V,
random_state=0)
for i in range(10):
for sample in X:
dict2.partial_fit(sample[np.newaxis, :])
assert not np.all(sparse_encode(X, dict1.components_, alpha=1) == 0)
assert_array_almost_equal(dict1.components_, dict2.components_, decimal=2)
def test_unknown_method():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
with pytest.raises(ValueError):
sparse_encode(X, V, algorithm="<unknown>")