def test_support_least_square():
n_trials, n_channels, n_times = 2, 3, 100
n_times_atom, n_atoms = 10, 4
n_times_valid = n_times - n_times_atom + 1
reg = 0
X = np.random.randn(n_trials, n_channels, n_times)
uv = np.random.randn(n_atoms, n_channels + n_times_atom)
z = np.random.randn(n_trials, n_atoms, n_times_valid)
# The initial loss should be high
loss_0 = compute_X_and_objective_multi(X,
z_hat=z,
D_hat=uv,
reg=reg,
feasible_evaluation=False)
# The loss after updating z should be lower
z_hat, ztz, ztX = update_z_multi(X,
uv,
reg,
z0=z,
solver='l-bfgs',
solver_kwargs={'factr': 1e7})
loss_1 = compute_X_and_objective_multi(X,
z_hat=z_hat,
D_hat=uv,
reg=reg,
feasible_evaluation=False)
assert loss_1 < loss_0
# Here we recompute z on the support of z_hat, with reg=0
z_hat_2, ztz, ztX = update_z_multi(X,
uv,
reg=0,
z0=z_hat,
solver='l-bfgs',
solver_kwargs={'factr': 1e7},
freeze_support=True)
loss_2 = compute_X_and_objective_multi(X,
z_hat_2,
uv,
reg,
feasible_evaluation=False)
assert loss_2 <= loss_1 or np.isclose(loss_1, loss_2)
# Here we recompute z with reg=0, but with no support restriction
z_hat_3, ztz, ztX = update_z_multi(X,
uv,
reg=0,
z0=np.ones(z.shape),
solver='l-bfgs',
solver_kwargs={'factr': 1e7},
freeze_support=True)
loss_3 = compute_X_and_objective_multi(X,
z_hat_3,
uv,
reg,
feasible_evaluation=False)
assert loss_3 <= loss_2 or np.isclose(loss_3, loss_2)
def test_update_z_multi_decrease_cost_function(loss, solver):
n_trials, n_channels, n_times = 2, 3, 100
n_times_atom, n_atoms = 10, 4
n_times_valid = n_times - n_times_atom + 1
reg = 0
loss_params = dict(gamma=1, sakoe_chiba_band=n_times_atom // 2)
rng = np.random.RandomState(0)
X = rng.randn(n_trials, n_channels, n_times)
uv = rng.randn(n_atoms, n_channels + n_times_atom)
z = rng.randn(n_trials, n_atoms, n_times_valid)
if loss == 'whitening':
loss_params['ar_model'], X = whitening(X, ordar=10)
loss_0 = compute_X_and_objective_multi(X=X, z_hat=z, D_hat=uv, reg=reg,
feasible_evaluation=False,
loss=loss, loss_params=loss_params)
z_hat, ztz, ztX = update_z_multi(X, uv, reg, z0=z, solver=solver,
loss=loss, loss_params=loss_params,
return_ztz=True)
loss_1 = compute_X_and_objective_multi(X=X, z_hat=z_hat, D_hat=uv,
reg=reg, feasible_evaluation=False,
loss=loss, loss_params=loss_params)
assert loss_1 < loss_0
if loss == 'l2':
assert np.allclose(ztz, compute_ztz(z_hat, n_times_atom))
assert np.allclose(ztX, compute_ztX(z_hat, X))
def test_n_jobs_larger_than_n_trials():
n_trials, n_channels, n_times = 2, 3, 100
n_times_atom, n_atoms = 10, 4
rng = np.random.RandomState(0)
X = rng.randn(n_trials, n_channels, n_times)
uv = rng.randn(n_atoms, n_channels + n_times_atom)
z_hat, ztz, ztX = update_z_multi(X, uv, 0.1, n_jobs=3)
def test_support_least_square(solver_z):
n_trials, n_channels, n_times = 2, 3, 100
n_times_atom, n_atoms = 10, 4
n_times_valid = n_times - n_times_atom + 1
reg = 0.1
solver_kwargs = {'factr': 1e7}
rng = np.random.RandomState(0)
X = rng.randn(n_trials, n_channels, n_times)
uv = rng.randn(n_atoms, n_channels + n_times_atom)
z = rng.randn(n_trials, n_atoms, n_times_valid)
# The initial loss should be high
loss_0 = compute_X_and_objective_multi(X, z_hat=z, D_hat=uv, reg=0,
feasible_evaluation=False)
# The loss after updating z should be lower
z_hat, _, _ = update_z_multi(X, uv, reg, z0=z, solver=solver_z,
solver_kwargs=solver_kwargs)
loss_1 = compute_X_and_objective_multi(X, z_hat=z_hat, D_hat=uv, reg=0,
feasible_evaluation=False)
assert loss_1 < loss_0
# Here we recompute z on the support of z_hat, with reg=0
z_hat_2, _, _ = update_z_multi(X, uv, reg=0, z0=z_hat, solver=solver_z,
solver_kwargs=solver_kwargs,
freeze_support=True)
loss_2 = compute_X_and_objective_multi(X, z_hat_2, uv, 0,
feasible_evaluation=False)
assert loss_2 <= loss_1 or np.isclose(loss_1, loss_2)
# Here we recompute z with reg=0, but with no support restriction
z_hat_3, _, _ = update_z_multi(X, uv, reg=0, z0=z_hat_2,
solver=solver_z,
solver_kwargs=solver_kwargs,
freeze_support=True)
loss_3 = compute_X_and_objective_multi(X, z_hat_3, uv, 0,
feasible_evaluation=False)
assert loss_3 <= loss_2 or np.isclose(loss_3, loss_2)
def test_cd():
n_trials, n_channels, n_times = 5, 3, 100
n_times_atom, n_atoms = 10, 4
n_times_valid = n_times - n_times_atom + 1
reg = 1
rng = np.random.RandomState(0)
uv = rng.randn(n_atoms, n_channels + n_times_atom)
z = abs(rng.randn(n_trials, n_atoms, n_times_valid))
z_gen = abs(rng.randn(n_trials, n_atoms, n_times_valid))
z[z < 1] = 0
z_gen[z_gen < 1] = 0
z0 = z[0]
X = construct_X_multi(z_gen, D=uv, n_channels=n_channels)
loss_0 = compute_X_and_objective_multi(X=X,
z_hat=z_gen,
D_hat=uv,
reg=reg,
loss='l2',
feasible_evaluation=False)
constants = {}
constants['DtD'] = compute_DtD(uv, n_channels)
# Ensure that the initialization is good, by using a nearly optimal point
# and verifying that the cost does not goes up.
z_hat, ztz, ztX = update_z_multi(X,
D=uv,
reg=reg,
z0=z_gen,
solver="lgcd",
solver_kwargs={
'max_iter': 5,
'tol': 1e-5
},
return_ztz=True)
assert np.allclose(ztz, compute_ztz(z_hat, n_times_atom))
assert np.allclose(ztX, compute_ztX(z_hat, X))
loss_1 = compute_X_and_objective_multi(X=X,
z_hat=z_hat,
D_hat=uv,
reg=reg,
loss='l2',
feasible_evaluation=False)
assert loss_1 <= loss_0, "Bad initialization in greedy CD."
z_hat, pobj, times = _coordinate_descent_idx(X[0],
uv,
constants,
reg,
debug=True,
timing=True,
z0=z0,
max_iter=10000)
try:
assert all([p1 >= p2 for p1, p2 in zip(pobj[:-1], pobj[1:])]), "oups"
except AssertionError:
import matplotlib.pyplot as plt
plt.plot(pobj)
plt.show()
raise
def test_cd(use_sparse_lil):
n_trials, n_channels, n_times = 5, 3, 100
n_times_atom, n_atoms = 10, 4
n_times_valid = n_times - n_times_atom + 1
reg = 1
uv = np.random.randn(n_atoms, n_channels + n_times_atom)
if use_sparse_lil:
density = .1
z = [
sparse.random(n_atoms,
n_times_valid,
format='lil',
density=density) for _ in range(n_trials)
]
z_gen = [
sparse.random(n_atoms,
n_times_valid,
format='lil',
density=density) for _ in range(n_trials)
]
z0 = z[0]
else:
z = abs(np.random.randn(n_trials, n_atoms, n_times_valid))
z_gen = abs(np.random.randn(n_trials, n_atoms, n_times_valid))
z[z < 1] = 0
z_gen[z_gen < 1] = 0
z0 = z[0]
X = construct_X_multi(z_gen, D=uv, n_channels=n_channels)
loss_0 = compute_X_and_objective_multi(X=X,
z_hat=z_gen,
D_hat=uv,
reg=reg,
loss='l2',
feasible_evaluation=False)
constants = {}
constants['DtD'] = compute_DtD(uv, n_channels)
# Ensure that the initialization is good, by using a nearly optimal point
# and verifying that the cost does not goes up.
z_hat, ztz, ztX = update_z_multi(X,
D=uv,
reg=reg,
z0=z_gen,
solver="lgcd",
solver_kwargs={
'max_iter': 5,
'tol': 1e-5
})
if use_sparse_lil and cython_code._CYTHON_AVAILABLE:
from alphacsc.cython_code import _fast_compute_ztz, _fast_compute_ztX
assert np.allclose(ztz, _fast_compute_ztz(z_hat, n_times_atom))
assert np.allclose(ztX, _fast_compute_ztX(z_hat, X))
else:
assert np.allclose(ztz, compute_ztz(z_hat, n_times_atom))
assert np.allclose(ztX, compute_ztX(z_hat, X))
loss_1 = compute_X_and_objective_multi(X=X,
z_hat=z_hat,
D_hat=uv,
reg=reg,
loss='l2',
feasible_evaluation=False)
assert loss_1 <= loss_0, "Bad initialization in greedy CD."
z_hat, pobj, times = _coordinate_descent_idx(X[0],
uv,
constants,
reg,
debug=True,
timing=True,
z0=z0,
max_iter=10000)
try:
assert all([p1 >= p2 for p1, p2 in zip(pobj[:-1], pobj[1:])]), "oups"
except AssertionError:
import matplotlib.pyplot as plt
plt.plot(pobj)
plt.show()
raise