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import numpy as np
from .. import nmf
from nose.tools import assert_true, assert_false, raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
random_state = np.random.mtrand.RandomState(0)
@raises(ValueError)
def test_initialize_nn_input():
"""Test NNDSVD behaviour on negative input"""
nmf._initialize_nmf(-np.ones((2, 2)), 2)
def test_initialize_nn_output():
"""Test that NNDSVD does not return negative values"""
data = np.abs(random_state.randn(10, 10))
for var in (None, 'a', 'ar'):
W, H = nmf._initialize_nmf(data, 10)
assert_false((W < 0).any() or (H < 0).any())
def test_initialize_close():
"""Test NNDSVD error
Test that _initialize_nmf error is less than the standard deviation of the
entries in the matrix.
"""
A = np.abs(random_state.randn(10, 10))
W, H = nmf._initialize_nmf(A, 10)
error = np.linalg.norm(np.dot(W, H) - A)
sdev = np.linalg.norm(A - A.mean())
assert_true(error <= sdev)
def test_initialize_variants():
"""Test NNDSVD variants correctness
Test that the variants 'a' and 'ar' differ from basic NNDSVD only where
the basic version has zeros.
"""
data = np.abs(random_state.randn(10, 10))
W0, H0 = nmf._initialize_nmf(data, 10, variant=None)
Wa, Ha = nmf._initialize_nmf(data, 10, variant='a')
War, Har = nmf._initialize_nmf(data, 10, variant='ar')
for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
assert_true(np.allclose(evl[ref != 0], ref[ref != 0]))
@raises(ValueError)
def test_projgrad_nmf_fit_nn_input():
"""Test model fit behaviour on negative input"""
A = -np.ones((2, 2))
m = nmf.ProjectedGradientNMF(n_components=2, init=None)
m.fit(A)
def test_projgrad_nmf_fit_nn_output():
"""Test that the decomposition does not contain negative values"""
A = np.c_[5 * np.ones(5) - xrange(1, 6),
5 * np.ones(5) + xrange(1, 6)]
for init in (None, 'nndsvd', 'nndsvda', 'nndsvdar'):
model = nmf.ProjectedGradientNMF(n_components=2, init=init)
transf = model.fit_transform(A)
assert_false((model.components_ < 0).any() or
(transf < 0).any())
def test_projgrad_nmf_fit_close():
"""Test that the fit is not too far away"""
assert_true(nmf.ProjectedGradientNMF(5, init='nndsvda').fit(np.abs(
random_state.randn(6, 5))).reconstruction_err_ < 0.05)
@raises(ValueError)
def test_nls_nn_input():
"""Test NLS solver's behaviour on negative input"""
A = np.ones((2, 2))
nmf._nls_subproblem(A, A, -A, 0.001, 20)
def test_nls_nn_output():
"""Test that NLS solver doesn't return negative values"""
A = np.atleast_2d(range(1, 5))
Ap, _, _ = nmf._nls_subproblem(np.dot(A.T, -A), A.T, A, 0.001, 100)
assert_false((Ap < 0).any())
def test_nls_close():
"""Test that the NLS results should be close"""
A = np.atleast_2d(range(1, 5))
Ap, _, _ = nmf._nls_subproblem(np.dot(A.T, A), A.T, np.zeros_like(A),
0.001, 100)
assert_true((np.abs(Ap - A) < 0.01).all())
def test_projgrad_nmf_transform():
"""Test that NMF.transform returns close values
(transform uses scipy.optimize.nnls for now)
"""
A = np.abs(random_state.randn(6, 5))
m = nmf.ProjectedGradientNMF(n_components=5, init='nndsvd')
transf = m.fit_transform(A)
assert_true(np.allclose(transf, m.transform(A), atol=1e-2, rtol=0))
def test_projgrad_nmf_sparseness():
"""Test sparseness
Test that sparsity contraints actually increase sparseness in the
part where they are applied.
"""
A = np.abs(random_state.randn(10, 10))
m = nmf.ProjectedGradientNMF(n_components=5).fit(A)
data_sp = nmf.ProjectedGradientNMF(n_components=5,
sparseness='data').fit(A).data_sparseness_
comp_sp = nmf.ProjectedGradientNMF(n_components=5,
sparseness='components').fit(A).comp_sparseness_
assert_greater(data_sp, m.data_sparseness_)
assert_greater(comp_sp, m.comp_sparseness_)
def test_sparse_input():
"""Test that sparse matrices are accepted as input"""
from scipy.sparse import csr_matrix
A = np.abs(random_state.randn(10, 10))
A[:, 2 * np.arange(5)] = 0
T1 = nmf.ProjectedGradientNMF(n_components=5, init=999).fit_transform(A)
A = csr_matrix(A)
T2 = nmf.ProjectedGradientNMF(n_components=5, init=999).fit_transform(A)
assert_array_almost_equal(T1, T2)
if __name__ == '__main__':
import nose
nose.run(argv=['', __file__])
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