def test_minibatch_nmf_verbose():
# Check verbose mode of MiniBatchNMF for better coverage.
A = np.random.RandomState(0).random_sample((100, 10))
nmf = MiniBatchNMF(tol=1e-2, random_state=0, verbose=1)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
nmf.fit(A)
finally:
sys.stdout = old_stdout
def test_minibatch_nmf_negative_beta_loss(beta_loss):
"""Check that an error is raised if beta_loss < 0 and X contains zeros."""
rng = np.random.RandomState(0)
X = rng.normal(size=(6, 5))
X[X < 0] = 0
nmf = MiniBatchNMF(beta_loss=beta_loss, random_state=0)
msg = "When beta_loss <= 0 and X contains zeros, the solver may diverge."
with pytest.raises(ValueError, match=msg):
nmf.fit(X)
def test_nmf_dtype_match(dtype_in, dtype_out, solver):
# Check that NMF preserves dtype (float32 and float64)
X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False)
np.abs(X, out=X)
nmf = NMF(solver=solver)
assert nmf.fit(X).transform(X).dtype == dtype_out
assert nmf.fit_transform(X).dtype == dtype_out
assert nmf.components_.dtype == dtype_out
def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out):
# Check that NMF preserves dtype (float32 and float64)
X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False)
np.abs(X, out=X)
nmf = Estimator(alpha_W=1.0, alpha_H=1.0, tol=1e-2, random_state=0, **solver)
assert nmf.fit(X).transform(X).dtype == dtype_out
assert nmf.fit_transform(X).dtype == dtype_out
assert nmf.components_.dtype == dtype_out
def test_nmf_dtype_match(dtype_in, dtype_out, solver, regularization):
# Check that NMF preserves dtype (float32 and float64)
X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False)
np.abs(X, out=X)
# FIXME : should be removed in 1.1
init = 'nndsvda'
nmf = NMF(solver=solver, regularization=regularization, init=init)
assert nmf.fit(X).transform(X).dtype == dtype_out
assert nmf.fit_transform(X).dtype == dtype_out
assert nmf.components_.dtype == dtype_out
