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)
def _construct_prox_obj(self, coeffs=None, project=False):
n_penalized_features = len(self.penalized_features) \
if self.penalized_features is not None else 0
if project:
# project future _coeffs on the support of given _coeffs
if all(self.n_lags == 0):
proxs = [ProxZero()]
elif coeffs is not None:
prox_ranges = self._detect_support(coeffs)
proxs = [ProxEquality(0, range=r) for r in prox_ranges]
else:
raise ValueError("Coeffs are None. " +
"Equality penalty cannot infer the "
"coefficients support.")
elif n_penalized_features > 0 and self._C_tv is not None or \
self._C_group_l1 is not None:
# TV and GroupLasso penalties
blocks_start = np.zeros(n_penalized_features)
blocks_end = np.zeros(n_penalized_features)
proxs = []
for i in self.penalized_features:
start = int(self._features_offset[i])
blocks_start[i] = start
end = int(blocks_start[i] + self._n_lags[i] + 1)
blocks_end[i] = end
if self._C_tv is not None:
proxs.append(ProxTV(1 / self._C_tv, range=(start, end)))
if self._C_group_l1 is not None:
blocks_size = blocks_end - blocks_start
proxs.append(
ProxGroupL1(1 / self._C_group_l1, blocks_start.tolist(),
blocks_size.tolist()))
else:
# Default prox: does nothing
proxs = [ProxZero()]
prox_obj = ProxMulti(tuple(proxs))
return prox_obj
def test_ProxEquality(self):
"""...Test of ProxEquality
"""
coeffs = self.coeffs.copy()
strength = 0.5
prox = ProxEquality(strength)
self.assertIsNone(prox.strength)
prox.strength = 2.
self.assertIsNone(prox.strength)
self.assertEqual(prox.value(coeffs), np.inf)
out = np.empty(coeffs.shape)
out.fill(np.mean(coeffs))
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
step = 4.
np.testing.assert_array_almost_equal(prox.call(coeffs, step=step), out)
coeffs -= 10.
out.fill(np.mean(coeffs))
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
prox.positive = True
out.fill(0.)
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
prox.range = (3, 8)
prox.positive = False
coeffs = self.coeffs.copy()
out = coeffs.copy()
out[3:8] = np.mean(out[3:8])
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
coeffs[3:8] -= 10.
out[3:8].fill(np.mean(coeffs[3:8]))
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
prox.positive = True
out[3:8].fill(0.)
np.testing.assert_array_almost_equal(prox.call(coeffs), out)
self.assertEqual(prox.value(out), 0)
self.assertEqual(prox.value(coeffs), np.inf)
ProxEquality, ProxL1w
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))