def test_ProxL1w(self):
"""...Test of test_ProxL1w
"""
coeffs = self.coeffs.copy().astype(self.dtype)
l_l1 = 3e-2
t = 1.7
weights = np.arange(coeffs.shape[0], dtype=np.double)
prox = ProxL1w(l_l1, weights).astype(self.dtype)
thresh = t * l_l1 * weights
out = np.sign(coeffs) * (np.abs(coeffs) - thresh) \
* (np.abs(coeffs) > thresh)
self.assertAlmostEqual(
prox.value(coeffs),
l_l1 * (weights * np.abs(coeffs)).sum(), delta=self.delta)
assert_almost_equal(
prox.call(coeffs, step=t), out, decimal=self.decimal_places)
weights = np.arange(coeffs.shape[0], dtype=np.double)[3:8]
prox = ProxL1w(l_l1, weights, (3, 8)).astype(self.dtype)
thresh = t * l_l1 * weights
sub_coeffs = coeffs[3:8]
out = coeffs.copy()
out[3:8] = np.sign(sub_coeffs) \
* (np.abs(sub_coeffs) - thresh) \
* (np.abs(sub_coeffs) > thresh)
self.assertAlmostEqual(
prox.value(coeffs),
l_l1 * (weights * np.abs(coeffs[3:8])).sum(), delta=self.delta)
assert_almost_equal(
prox.call(coeffs, step=t), out, decimal=self.decimal_places)
weights = np.arange(coeffs.shape[0], dtype=np.double)[3:8]
prox = ProxL1w(l_l1, weights, range=(3, 8),
positive=True).astype(self.dtype)
thresh = t * l_l1 * weights
out = coeffs.copy()
out[3:8] = np.sign(sub_coeffs) \
* (np.abs(sub_coeffs) - thresh) \
* (np.abs(sub_coeffs) > thresh)
idx = out[3:8] < 0
out[3:8][idx] = 0
self.assertAlmostEqual(
prox.value(coeffs),
l_l1 * (weights * np.abs(coeffs[3:8])).sum(), delta=self.delta)
assert_almost_equal(
prox.call(coeffs, step=t), out, decimal=self.decimal_places)
def test_dense_and_sparse_match(self):
"""...Test in SVRG that dense and sparse code matches in all possible
settings
"""
variance_reductions = ['last', 'rand']
rand_types = ['perm', 'unif']
seed = 123
tol = 0.
max_iter = 50
n_samples = 500
n_features = 20
# Crazy prox examples
proxs = [
ProxTV(strength=1e-2, range=(5, 13),
positive=True).astype(self.dtype),
ProxElasticNet(strength=1e-2, ratio=0.9).astype(self.dtype),
ProxEquality(range=(0, n_features)).astype(self.dtype),
ProxL1(strength=1e-3, range=(5, 17)).astype(self.dtype),
ProxL1w(strength=1e-3, weights=np.arange(5, 17, dtype=np.double),
range=(5, 17)).astype(self.dtype),
]
for intercept in [-1, None]:
X, y = self.simu_linreg_data(dtype=self.dtype, interc=intercept,
n_features=n_features,
n_samples=n_samples)
fit_intercept = intercept is not None
model_dense, model_spars = self.get_dense_and_sparse_linreg_model(
X, y, dtype=self.dtype, fit_intercept=fit_intercept)
step = 1 / model_spars.get_lip_max()
for variance_reduction, rand_type, prox in product(
variance_reductions, rand_types, proxs):
solver_sparse = SVRG(step=step, tol=tol, max_iter=max_iter,
verbose=False,
variance_reduction=variance_reduction,
rand_type=rand_type, seed=seed)
solver_sparse.set_model(model_spars).set_prox(prox)
solver_dense = SVRG(step=step, tol=tol, max_iter=max_iter,
verbose=False,
variance_reduction=variance_reduction,
rand_type=rand_type, seed=seed)
solver_dense.set_model(model_dense).set_prox(prox)
solver_sparse.solve()
solver_dense.solve()
places = 7
if self.dtype is "float32":
places = 3
np.testing.assert_array_almost_equal(solver_sparse.solution,
solver_dense.solution,
decimal=places)
np.random.seed(12)
x = np.random.randn(50)
a, b = x.min() - 1e-1, x.max() + 1e-1
s = 0.4
proxs = [
ProxZero(),
ProxPositive(),
ProxL2Sq(strength=s),
ProxL1(strength=s),
ProxElasticNet(strength=s, ratio=0.5),
ProxSlope(strength=s),
ProxTV(strength=s),
ProxEquality(range=(25, 40)),
ProxL1w(strength=s, weights=0.1 * np.arange(50, dtype=np.double)),
ProxGroupL1(strength=2 * s,
blocks_start=np.arange(0, 50, 10),
blocks_length=10 * np.ones((5, ))),
ProxBinarsity(strength=s,
blocks_start=np.arange(0, 50, 10),
blocks_length=10 * np.ones((5, )))
]
fig, _ = plt.subplots(3, 4, figsize=(16, 12), sharey=True, sharex=True)
fig.axes[0].stem(x)
fig.axes[0].set_title("original vector", fontsize=16)
fig.axes[0].set_xlim((-1, 51))
fig.axes[0].set_ylim((a, b))
for i, prox in enumerate(proxs):