def test_nmf_minibatchnmf_equivalence(beta_loss):
# Test that MiniBatchNMF is equivalent to NMF when batch_size = n_samples and
# forget_factor 0.0 (stopping criterion put aside)
rng = np.random.mtrand.RandomState(42)
X = np.abs(rng.randn(48, 5))
nmf = NMF(
n_components=5,
beta_loss=beta_loss,
solver="mu",
random_state=0,
tol=0,
)
mbnmf = MiniBatchNMF(
n_components=5,
beta_loss=beta_loss,
random_state=0,
tol=0,
max_no_improvement=None,
batch_size=X.shape[0],
forget_factor=0.0,
)
W = nmf.fit_transform(X)
mbW = mbnmf.fit_transform(X)
assert_allclose(W, mbW)
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
def test_mbnmf_inverse_transform():
# Test that MiniBatchNMF.transform followed by MiniBatchNMF.inverse_transform
# is close to the identity
rng = np.random.RandomState(0)
A = np.abs(rng.randn(6, 4))
nmf = MiniBatchNMF(
random_state=rng,
max_iter=500,
init="nndsvdar",
fresh_restarts=True,
)
ft = nmf.fit_transform(A)
A_new = nmf.inverse_transform(ft)
assert_allclose(A, A_new, rtol=1e-3, atol=1e-2)