def test_dict_learning_shapes():
n_components = 5
dico = DictionaryLearning(n_components, random_state=0).fit(X)
assert dico.components_.shape == (n_components, n_features)
n_components = 1
dico = DictionaryLearning(n_components, random_state=0).fit(X)
assert dico.components_.shape == (n_components, n_features)
assert dico.transform(X).shape == (X.shape[0], n_components)
def test_dict_learning_split():
n_components = 5
dico = DictionaryLearning(n_components,
transform_algorithm='threshold',
random_state=0)
code = dico.fit(X).transform(X)
dico.split_sign = True
split_code = dico.transform(X)
assert_array_almost_equal(
split_code[:, :n_components] - split_code[:, n_components:], code)
def test_dict_learning_lars_code_positivity():
n_components = 5
dico = DictionaryLearning(n_components,
transform_algorithm="lars",
random_state=0,
positive_code=True,
fit_algorithm="cd").fit(X)
err_msg = "Positive constraint not supported for '{}' coding method."
err_msg = err_msg.format("lars")
with pytest.raises(ValueError, match=err_msg):
dico.transform(X)
def test_dict_learning_lassocd_readonly_data():
n_components = 12
with TempMemmap(X) as X_read_only:
dico = DictionaryLearning(n_components,
transform_algorithm='lasso_cd',
transform_alpha=0.001,
random_state=0,
n_jobs=4)
with ignore_warnings(category=ConvergenceWarning):
code = dico.fit(X_read_only).transform(X_read_only)
assert_array_almost_equal(np.dot(code, dico.components_),
X_read_only,
decimal=2)
def test_dict_learning_positivity(transform_algorithm, positive_code,
positive_dict):
n_components = 5
dico = DictionaryLearning(n_components,
transform_algorithm=transform_algorithm,
random_state=0,
positive_code=positive_code,
positive_dict=positive_dict,
fit_algorithm="cd").fit(X)
code = dico.transform(X)
if positive_dict:
assert (dico.components_ >= 0).all()
else:
assert (dico.components_ < 0).any()
if positive_code:
assert (code >= 0).all()
else:
assert (code < 0).any()
def test_dict_learning_lars_dict_positivity(positive_dict):
n_components = 5
dico = DictionaryLearning(n_components,
transform_algorithm="lars",
random_state=0,
positive_dict=positive_dict,
fit_algorithm="cd").fit(X)
if positive_dict:
assert (dico.components_ >= 0).all()
else:
assert (dico.components_ < 0).any()
def test_dict_learning_nonzero_coefs():
n_components = 4
dico = DictionaryLearning(n_components,
transform_algorithm='lars',
transform_n_nonzero_coefs=3,
random_state=0)
code = dico.fit(X).transform(X[np.newaxis, 1])
assert len(np.flatnonzero(code)) == 3
dico.set_params(transform_algorithm='omp')
code = dico.transform(X[np.newaxis, 1])
assert len(np.flatnonzero(code)) == 3
def test_dict_learning_reconstruction():
n_components = 12
dico = DictionaryLearning(n_components,
transform_algorithm='omp',
transform_alpha=0.001,
random_state=0)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
def test_dict_learning_reconstruction_parallel():
# regression test that parallel reconstruction works with n_jobs>1
n_components = 12
dico = DictionaryLearning(n_components,
transform_algorithm='omp',
transform_alpha=0.001,
random_state=0,
n_jobs=4)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)
dico.set_params(transform_algorithm='lasso_lars')
code = dico.transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)
def test_dict_learning_overcomplete():
n_components = 12
dico = DictionaryLearning(n_components, random_state=0).fit(X)
assert dico.components_.shape == (n_components, n_features)
def test_dict_learning_unknown_fit_algorithm():
n_components = 5
dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
with pytest.raises(ValueError):
dico.fit(X)