def test_nmf_regularization():
# Test the effect of L1 and L2 regularizations
n_samples = 6
n_features = 5
n_components = 3
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(n_samples, n_features))
# L1 regularization should increase the number of zeros
l1_ratio = 1.
for solver in ['cd', 'mu']:
regul = nmf.NMF(n_components=n_components,
solver=solver,
alpha=0.5,
l1_ratio=l1_ratio,
random_state=42)
model = nmf.NMF(n_components=n_components,
solver=solver,
alpha=0.,
l1_ratio=l1_ratio,
random_state=42)
W_regul = regul.fit_transform(X)
W_model = model.fit_transform(X)
H_regul = regul.components_
H_model = model.components_
W_regul_n_zeros = W_regul[W_regul == 0].size
W_model_n_zeros = W_model[W_model == 0].size
H_regul_n_zeros = H_regul[H_regul == 0].size
H_model_n_zeros = H_model[H_model == 0].size
assert W_regul_n_zeros > W_model_n_zeros
assert H_regul_n_zeros > H_model_n_zeros
# L2 regularization should decrease the mean of the coefficients
l1_ratio = 0.
for solver in ['cd', 'mu']:
regul = nmf.NMF(n_components=n_components,
solver=solver,
alpha=0.5,
l1_ratio=l1_ratio,
random_state=42)
model = nmf.NMF(n_components=n_components,
solver=solver,
alpha=0.,
l1_ratio=l1_ratio,
random_state=42)
W_regul = regul.fit_transform(X)
W_model = model.fit_transform(X)
H_regul = regul.components_
H_model = model.components_
assert W_model.mean() > W_regul.mean()
assert H_model.mean() > H_regul.mean()
def test_nmf_regularization(solver):
# Test the effect of L1 and L2 regularizations
n_samples = 6
n_features = 5
n_components = 3
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(n_samples, n_features))
# FIXME : should be removed in 1.1
init = "nndsvda"
# L1 regularization should increase the number of zeros
l1_ratio = 1.0
regul = nmf.NMF(
n_components=n_components,
solver=solver,
alpha_W=0.5,
l1_ratio=l1_ratio,
random_state=42,
init=init,
)
model = nmf.NMF(
n_components=n_components,
solver=solver,
alpha_W=0.0,
l1_ratio=l1_ratio,
random_state=42,
init=init,
)
W_regul = regul.fit_transform(X)
W_model = model.fit_transform(X)
H_regul = regul.components_
H_model = model.components_
W_regul_n_zeros = W_regul[W_regul == 0].size
W_model_n_zeros = W_model[W_model == 0].size
H_regul_n_zeros = H_regul[H_regul == 0].size
H_model_n_zeros = H_model[H_model == 0].size
assert W_regul_n_zeros > W_model_n_zeros
assert H_regul_n_zeros > H_model_n_zeros
# L2 regularization should decrease the mean of the coefficients
l1_ratio = 0.0
regul = nmf.NMF(
n_components=n_components,
solver=solver,
alpha_W=0.5,
l1_ratio=l1_ratio,
random_state=42,
init=init,
)
model = nmf.NMF(
n_components=n_components,
solver=solver,
alpha_W=0.0,
l1_ratio=l1_ratio,
random_state=42,
init=init,
)
W_regul = regul.fit_transform(X)
W_model = model.fit_transform(X)
H_regul = regul.components_
H_model = model.components_
assert (linalg.norm(W_model))**2.0 + (linalg.norm(H_model))**2.0 > (
linalg.norm(W_regul))**2.0 + (linalg.norm(H_regul))**2.0