def test_PowerMethod_init_row_sampling_factor():
with pytest.raises(ValueError):
_ = PowerMethod(init_row_sampling_factor=0)
PM1 = PowerMethod(init_row_sampling_factor=4)
array = np.random.rand(100, 100)
_, _, _ = PM1.svd(array)
def test_PowerMethod_case1():
n = 100
p = 80
array = np.random.rand(100, 80)
mu = array.mean(axis=0)
std = np.diag(1 / array.std(axis=0))
scaled_centered_array = (array - mu).dot(std)
U, S, V = np.linalg.svd(scaled_centered_array,
full_matrices=False) # Ground Truth
array = make_snp_array(da.array(array),
mean=True,
std=True,
std_method='norm',
mask_nan=False,
dtype='float64')
for k in range(1, 10):
U_k, S_k, V_k = U[:, :k], S[:k], V[:k, :]
PM = PowerMethod(k=k,
tol=1e-9,
scoring_method='rmse',
max_iter=100,
sub_svd_start=False,
init_row_sampling_factor=1,
factor=None,
lmbd=0)
U_k_PM, S_k_PM, V_k_PM = PM.svd(array)
np.testing.assert_array_almost_equal(S_k, S_k_PM)
assert V_k.shape == V_k_PM.shape == (k, p)
assert U_k.shape == U_k_PM.shape == (n, k)
np.testing.assert_almost_equal(subspace_dist(V_k, V_k_PM, S_k_PM), 0)
np.testing.assert_almost_equal(subspace_dist(U_k, U_k_PM, S_k_PM), 0)
def test_PowerMethod_nan_arrays_fills():
array = np.random.randint(0, 3, size=(100, 100)).astype(float)
array[0, 0] = 10000
median = round(np.median(array))
mean = round(np.mean(array))
k = 10
for method in ['mean', 'median', 10]:
PM = PowerMethod(factor=None,
scale=False,
center=False,
tol=1e-16,
lmbd=0)
if method == 'mean':
filled_value = mean
elif method == 'median':
filled_value = median
else:
filled_value = method
array[0, 1] = filled_value
U, S, V = np.linalg.svd(array, full_matrices=False)
U_k, S_k, V_k = svd_to_trunc_svd(U, S, V, k=k)
array[0, 1] = float('nan')
U, S, V = PM.svd(da.array(array), mask_fill=method, mask_nan=True)
assert PM.array.array[0, 1] == filled_value
np.testing.assert_array_almost_equal(S, S_k)
def test_PowerMethod_std_method():
N = 1000
P = 100
k = 10
array = da.random.randint(0, 3, size=(N, P))
for method in ['norm', 'binom']:
new_array = make_snp_array(array, std_method=method)
PM = PowerMethod(k=k, scoring_method='q-vals', tol=1e-13, factor=None)
U_PM, S_PM, V_PM = PM.svd(array=new_array)
mean = array.mean(axis=0)
if method == 'norm':
std = array.std(axis=0)
else:
p = mean / 2
std = da.sqrt(2 * p * (1 - p))
x = (array - mean).dot(np.diag(1 / std))
U, S, V = da.linalg.svd(x)
U_k, S_k, V_k = svd_to_trunc_svd(U, S, V, k=k)
np.testing.assert_almost_equal(subspace_dist(U_PM, U_k, S_k),
0,
decimal=3)
np.testing.assert_almost_equal(subspace_dist(V_PM, V_k, S_k),
0,
decimal=3)
np.testing.assert_array_almost_equal(S_k, S_PM, decimal=2)
def test_PowerMethod_project():
N, P = 1000, 1000
k = 10
svd_array = da.random.random(size=(N, P)).persist()
proj_array = da.random.random(size=(10, P)).persist()
mu = da.mean(svd_array, axis=0).persist()
std = da.diag(1 / da.std(svd_array, axis=0)).persist()
for scale in [True, False]:
for center in [True, False]:
svd_array1 = svd_array
proj_array1 = proj_array
if center:
svd_array1 = svd_array1 - mu
proj_array1 = proj_array1 - mu
if scale:
svd_array1 = svd_array1.dot(std)
proj_array1 = proj_array1.dot(std)
U, S, V = da.linalg.svd(svd_array1)
U_k, S_k, V_k = svd_to_trunc_svd(U, S, V, k=k)
PM = PowerMethod(k=k,
scale=scale,
center=center,
factor=None,
tol=1e-12)
U_PM, S_PM, V_PM = PM.svd(array=svd_array)
np.testing.assert_array_almost_equal(
PM.project(proj_array, onto=V_k.T), proj_array1.dot(V_k.T))
def test_PowerMethod_return_history():
max_iter = 5
array = da.random.random(size=(1000, 1000))
PM = PowerMethod(scoring_method=['rmse', 'q-vals', 'v-subspace'],
max_iter=max_iter,
tol=[1e-16, 1e-16, 1e-16])
history = PM.svd(array, return_history=True)
assert len(history.acc['q-vals']) == max_iter
assert len(history.acc['rmse']) == max_iter
assert len(history.acc['v-subspace']) == max_iter
assert len(history.iter['U']) == 0
assert len(history.iter['S']) == max_iter
assert len(history.iter['V']) == max_iter
assert len(history.iter['last_value']) == 3
assert history.time['start'] < history.time['stop']
assert len(history.time['step']) == max_iter
assert len(history.time['acc']) == max_iter
np.testing.assert_array_almost_equal(np.array(history.time['iter']),
np.array(history.time['acc']) +
np.array(history.time['step']),
decimal=3)
def test_PowerMethod_reset():
for start in [True, False]:
PM = PowerMethod(sub_svd_start=start)
_, _, _ = PM.svd(da.array(np.random.randn(100, 50)))
_, _, _ = PM.svd(da.array(np.random.randn(110, 60)))
def test_PowerMethod_persist():
N = 10000
x = da.random.random((N, N))
PM = PowerMethod(max_iter=1)
_, _, _ = PM.svd(x, persist=False)
_, _, _ = PM.svd(x, persist=True)
for dt in ['float64', 'float32', 'int8', 'uint8', 'int64', 'uint64']:
_, _, _ = PM.svd(x, persist=True, dtype=dt)
def test_PowerMethod_time_limit():
with pytest.raises(ValueError):
_ = PowerMethod(time_limit=0)
PM = PowerMethod(time_limit=10, max_iter=int(1e10), tol=1e-20)
array = np.random.rand(100, 100)
_, _, _ = PM.svd(array)
assert 9 <= PM.time <= 11
def test_PowerMethod_all_tols_agree():
n = 100
p = 80
k = 10
array = np.random.rand(n, p)
PM = PowerMethod(k=k,
tol=1e-9,
scoring_method='q-vals',
max_iter=100,
lmbd=0)
U_q, S_q, V_q = PM.svd(array)
PM = PowerMethod(k=k,
tol=1e-4,
scoring_method='rmse',
max_iter=100,
lmbd=0)
U_r, S_r, V_r = PM.svd(array)
PM = PowerMethod(k=k,
tol=1e-9,
scoring_method='v-subspace',
max_iter=100,
lmbd=0)
U_v, S_v, V_v = PM.svd(array)
np.testing.assert_array_almost_equal(S_q, S_r)
np.testing.assert_array_almost_equal(S_q, S_v)
np.testing.assert_almost_equal(subspace_dist(U_q, U_r, S_q), 0)
np.testing.assert_almost_equal(subspace_dist(U_q, U_v, S_q), 0)
np.testing.assert_almost_equal(subspace_dist(V_q, V_r, S_q), 0)
np.testing.assert_almost_equal(subspace_dist(V_q, V_v, S_q), 0)
def test_PowerMethod_max_iter():
with pytest.raises(ValueError):
_ = PowerMethod(max_iter=0)
PM = PowerMethod(max_iter=1)
array = np.random.rand(100, 100)
_, _, _ = PM.svd(array)
assert PM.num_iter == 1
assert len(PM.history.iter['S']) == 1
def test_PowerMethod_k():
with pytest.raises(ValueError):
_ = PowerMethod(k=0)
with pytest.raises(ValueError):
_ = PowerMethod(k=-1)
PM = PowerMethod(k=100)
array = np.random.rand(50, 50)
with pytest.raises(ValueError):
_, _, _ = PM.svd(array)
def test_PowerMethod_nan_arrays():
array = np.random.randn(100, 100)
for bad_type in [float('nan')]:
array[0, 0] = bad_type
for start in [True, False]:
PM = PowerMethod(sub_svd_start=start, max_iter=2)
with pytest.raises(np.linalg.LinAlgError):
_, _, _ = PM.svd(da.array(array))
clean_array = make_snp_array(da.array(array),
mask_nan=True,
std_method='norm',
dtype='float64')
_, _, _ = PM.svd(clean_array)
def test_PowerMethod_subsvd_finds_eigenvectors_failure():
N = 1000
k = 10
s_orig = np.array([1.01**i for i in range(1, N + 1)])
array = da.diag(s_orig)
PM = PowerMethod(tol=1e-16, lmbd=0, max_iter=100)
U_PM, S_PM, V_PM = PM.svd(array)
with pytest.raises(AssertionError):
np.testing.assert_array_almost_equal(s_orig[-k:][::-1],
S_PM,
decimal=0)
def test_PowerMethod_transpose_array_shape():
N, P, K = 100, 200, 10
array = da.array(np.random.rand(N, P))
PM = PowerMethod(k=K)
U_PM, S_PM, V_PM = PM.svd(array)
assert U_PM.shape == (N, K)
assert S_PM.shape == (K, )
assert V_PM.shape == (K, P)
U_PM, S_PM, V_PM = PM.svd(array)
assert U_PM.shape == (P, K)
assert S_PM.shape == (K, )
assert V_PM.shape == (K, N)
def test_PowerMethod_buffer():
with pytest.raises(ValueError):
_ = PowerMethod(buffer=-1)
array = np.random.rand(1000, 1000)
U, S, V = np.linalg.svd(array)
S[0:15] = 1e3 + S[0:15]
array = U.dot(np.diag(S).dot(V))
for method in ['q-vals', 'rmse', 'v-subspace']:
PM1 = PowerMethod(buffer=2,
max_iter=3,
tol=1e-16,
scoring_method=method)
PM2 = PowerMethod(buffer=30,
max_iter=3,
tol=1e-16,
scoring_method=method)
_, _, _ = PM1.svd(array)
_, _, _ = PM2.svd(array)
if method == 'v-subspace':
# V-Subspace for this convergese quickly due small size of test case
assert np.abs(PM1.history.acc[method][-1] -
PM2.history.acc[method][-1]) < 1e-4
else:
assert PM1.history.acc[method][-1] > PM2.history.acc[method][-1]
def test_PowerMethod_subsvd_finds_eigenvectors():
N = 1000
k = 10
s_orig = np.array([1.01**i for i in range(1, N + 1)])
array = da.diag(s_orig)
PM = PowerMethod(tol=1e-16, factor=None, lmbd=.1, max_iter=100)
U_PM, S_PM, V_PM = PM.svd(array)
np.testing.assert_array_almost_equal(s_orig[-k:][::-1], S_PM,
decimal=0) # Max Q-Val is 21,000
v = np.zeros_like(V_PM).compute()
for j in range(k):
v[j, N - k + j] = 1
np.testing.assert_almost_equal(subspace_dist(V_PM, v, S_PM), 0, decimal=5)
def test_PowerMethod_case2():
array = np.random.rand(100, 100)
mu = array.mean(axis=0)
std = np.diag(1 / array.std(axis=0))
scaled_centered_array = (array - mu).dot(std)
array = make_snp_array(da.array(array),
mean=True,
std=True,
std_method='norm',
mask_nan=False,
dtype='float64')
U, S, V = np.linalg.svd(scaled_centered_array.dot(scaled_centered_array.T),
full_matrices=False) # Ground Truth
_, _, V = np.linalg.svd(scaled_centered_array.T.dot(scaled_centered_array),
full_matrices=False)
S = np.sqrt(S)
k = 10
U_k, S_k, V_k = U[:, :k], S[:k], V[:k, :]
previous_S_error = float('inf')
previous_U_error = float('inf')
previous_V_error = float('inf')
for t in np.logspace(0, -12, 20):
PM = PowerMethod(k=k,
tol=t,
scoring_method='q-vals',
max_iter=100,
factor=None,
lmbd=0)
U_k_PM, S_k_PM, V_k_PM = PM.svd(array)
assert subspace_dist(U_k, U_k_PM, S_k) <= previous_U_error
assert subspace_dist(V_k, V_k_PM, S_k) <= previous_V_error
assert np.linalg.norm(S_k - S_k_PM) <= previous_S_error
previous_S_error = np.linalg.norm(S_k - S_k_PM)
previous_U_error = subspace_dist(U_k, U_k_PM, S_k)
previous_V_error = subspace_dist(V_k, V_k_PM, S_k)
assert subspace_dist(U_k, U_k_PM, S_k) <= 1e-9
assert subspace_dist(V_k, V_k_PM, S_k) <= 1e-9
assert np.linalg.norm(S_k - S_k_PM) <= 1e-12
def test_PowerMethod_multi_tol():
array = np.random.rand(100, 70)
k = 10
tol_groups = [[1e-1, 1e-1, 1e-1], [1e-16, 1e-4, 1 - 16],
[1e-6, 1e-16, 1e-16], [1e-16, 1e-16, 1e-6]]
for tols in tol_groups:
PM = PowerMethod(k=k,
tol=tols,
scoring_method=['q-vals', 'rmse', 'v-subspace'],
factor=None,
lmbd=0)
_, _, _ = PM.svd(array)
assert (min(PM.history.acc['q-vals']) <= tols[0]
or min(PM.history.acc['rmse']) <= tols[1]
or min(PM.history.acc['v-subspace']) <= tols[2])
fail_test_tols = [1e-6, 1e-16, 1e-16]
PM = PowerMethod(k=k,
tol=fail_test_tols,
scoring_method=['q-vals', 'rmse', 'v-subspace'],
factor=None,
lmbd=0)
_, _, _ = PM.svd(array)
assert (min(PM.history.acc['q-vals']) <= fail_test_tols[0]
and min(PM.history.acc['rmse']) >= fail_test_tols[1]
and min(PM.history.acc['v-subspace']) >= fail_test_tols[2])
def test_PowerMethod_bad_arrays():
PM = PowerMethod()
with pytest.raises(ValueError):
PM.svd(np.random.rand(100))
with pytest.raises(ValueError):
PM.svd(np.random.rand(100, 100, 100))
def test_PowerMethod_scale_center():
array = np.random.rand(100, 70)
mu = array.mean(axis=0)
std = np.diag(1 / array.std(axis=0))
k = 10
for scale in [True, False]:
for center in [True, False]:
new_array = array
if center:
new_array = new_array - mu
if scale:
new_array = new_array.dot(std)
U, S, _ = np.linalg.svd(new_array.dot(new_array.T),
full_matrices=False) # Ground Truth
_, _, V = np.linalg.svd(new_array.T.dot(new_array),
full_matrices=False) # Ground Truth
S = np.sqrt(S)
U_k, S_k, V_k = U[:, :k], S[:k], V[:k, :]
snp_array = make_snp_array(da.array(array),
std=scale,
mean=center,
std_method='norm',
dtype='float64')
np.testing.assert_array_almost_equal(new_array, snp_array)
PM = PowerMethod(k=k,
tol=1e-12,
scoring_method='q-vals',
max_iter=100,
factor=None,
lmbd=0)
U_q, S_q, V_q = PM.svd(snp_array)
assert subspace_dist(U_k, U_q, S_k) <= 1e-8
assert subspace_dist(V_k, V_q, S_k) <= 1e-8
assert np.linalg.norm(S_k - S_q) <= 1e-9
def test_PowerMethod_factor():
n = 100
p = 80
array = np.random.rand(n, p)
sym_array = array.dot(array.T)
for f in ['n', 'p', None]:
if f == 'n':
factor = n
elif f == 'p':
factor = p
else:
factor = 1
U, S, V = np.linalg.svd(sym_array / factor, full_matrices=False)
S = np.sqrt(S)
k = 10
U_k, S_k, V_k = U[:, :k], S[:k], V[:k, :]
array = make_snp_array(da.array(array),
mean=False,
std=False,
std_method='norm',
mask_nan=False,
dtype='float64')
PM = PowerMethod(k=k,
tol=1e-9,
scoring_method='q-vals',
max_iter=100,
factor=f,
lmbd=0)
U_k_PM, S_k_PM, V_k_PM = PM.svd(array)
np.testing.assert_array_almost_equal(S_k, S_k_PM)
assert U_k.shape == U_k_PM.shape == (n, k)
np.testing.assert_almost_equal(subspace_dist(U_k, U_k_PM, S_k_PM), 0)
def test_PowerMethod_transpose_array():
array = da.array(np.random.rand(100, 200))
k = 10
U, S, V = da.linalg.svd(array)
U_k, S_k, V_k = svd_to_trunc_svd(U, S, V, k=k)
PM = PowerMethod(tol=1e-12,
k=k,
factor=None,
scale=False,
center=False,
lmbd=0)
U_PM, S_PM, V_PM = PM.svd(array)
np.testing.assert_array_almost_equal(S_k, S_PM)
np.testing.assert_almost_equal(subspace_dist(V_k, V_PM, S_PM), 0)
assert U_k.shape == U_PM.shape
U_PM, S_PM, V_PM = PM.svd(array, transpose=True)
np.testing.assert_almost_equal(subspace_dist(U_PM.T, V_k, S_PM), 0)
assert V_PM.shape != U_PM.shape
def test_PowerMethod_sub_svd_start():
array = np.random.rand(100, 100)
PM1 = PowerMethod(sub_svd_start=True, tol=1e-12, lmbd=0)
_, S1, _ = PM1.svd(array)
PM2 = PowerMethod(sub_svd_start=False, tol=1e-12, lmbd=0)
_, S2, _ = PM2.svd(array)
np.testing.assert_array_almost_equal(S1, S2)
def test_PowerMethod_p():
with pytest.raises(ValueError):
_ = PowerMethod(p=-1)
with pytest.raises(ValueError):
_ = PowerMethod(p=1)