def test_monitor():
model = Lasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward()
monitor = Monitor(callback=callback)
optimizer = LineSearch(n_outer=10, tol=tol)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor)
np.testing.assert_allclose(np.array(monitor.objs), np.array(objs))
def test_monitor():
model = Lasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward()
monitor = Monitor(callback=callback)
grad_search(algo,
criterion,
model,
X,
y,
np.log(alpha_max / 10),
monitor,
n_outer=10,
tol=tol)
np.testing.assert_allclose(np.array(monitor.objs), np.array(objs))
criterion = CrossVal(sub_criterion, cv=kf)
algo = ImplicitForward(tol_jac=1e-3)
# optimizer = LineSearch(n_outer=10, tol=tol)
if algo_name.startswith('implicit_forward'):
if algo_name == "implicit_forward_approx":
optimizer = GradientDescent(n_outer=30,
p_grad_norm=1.,
verbose=True,
tol=tol,
tol_decrease="geom")
else:
optimizer = GradientDescent(n_outer=30,
p_grad_norm=1.,
verbose=True,
tol=tol)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor)
elif algo_name == 'grid_search':
grid_search(algo,
criterion,
model,
X,
y,
None,
None,
monitor,
max_evals=20,
tol=tol,
alphas=alphas)
elif algo_name == 'random_search' or algo_name == 'bayesian':
hyperopt_wrapper(algo,
criterion,
estimator = sklearn.linear_model.Lasso(fit_intercept=False,
max_iter=1000,
warm_start=True,
tol=tol)
print('sparse-ho started')
t0 = time.time()
model = Lasso()
criterion = HeldOutMSE(None, None)
alpha0 = alpha_max / 10
monitor_grad = Monitor()
cross_val_criterion = CrossVal(criterion, cv=kf)
algo = ImplicitForward()
optimizer = LineSearch(n_outer=10, tol=tol)
grad_search(algo, cross_val_criterion, model, optimizer, X, y, alpha0,
monitor_grad)
t_grad_search = time.time() - t0
print('sparse-ho finished')
##############################################################################
# Plot results
# ------------
objs = reg.mse_path_.mean(axis=1)
p_alphas_grad = np.array(monitor_grad.alphas) / alpha_max
objs_grad = np.array(monitor_grad.objs)
print(f"Time for grid search: {t_sk:.2f} s")
print(f"Time for grad search (sparse-ho): {t_grad_search:.2f} s")