def test_val_grad():
#######################################################################
# Not all methods computes the full Jacobian, but all
# compute the gradients
# check that the gradient returned by all methods are the same
criterion = HeldOutMSE(idx_train, idx_val)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(model,
X,
y,
np.array(
[log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(
model,
X,
y,
np.array([log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000)
val_imp_fwd_custom, grad_imp_fwd_custom = criterion.get_val_grad(
model,
X,
y,
np.array([log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = Implicit()
val_imp, grad_imp = criterion.get_val_grad(model,
X,
y,
np.array(
[log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
np.testing.assert_allclose(val_fwd, val_imp_fwd)
np.testing.assert_allclose(grad_fwd, grad_imp_fwd)
np.testing.assert_allclose(val_imp_fwd, val_imp)
np.testing.assert_allclose(val_imp_fwd, val_imp_fwd_custom)
# for the implcit the conjugate grad does not converge
# hence the rtol=1e-2
np.testing.assert_allclose(grad_imp_fwd, grad_imp, atol=1e-3)
np.testing.assert_allclose(grad_imp_fwd, grad_imp_fwd_custom)
def test_grad_search(Optimizer, model, crit):
"""check that the paths are the same in the line search"""
n_outer = 2
criterion = HeldOutMSE(idx_train, idx_val)
monitor1 = Monitor()
algo = Forward()
optimizer = Optimizer(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor1)
criterion = HeldOutMSE(idx_train, idx_val)
monitor2 = Monitor()
algo = Implicit()
optimizer = Optimizer(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor2)
criterion = HeldOutMSE(idx_train, idx_val)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
optimizer = Optimizer(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor3)
np.testing.assert_allclose(np.array(monitor1.alphas),
np.array(monitor3.alphas))
np.testing.assert_allclose(np.array(monitor1.grads),
np.array(monitor3.grads),
rtol=1e-5)
np.testing.assert_allclose(np.array(monitor1.objs),
np.array(monitor3.objs))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
def test_val_grad(model):
criterion = HeldOutLogistic(idx_val, idx_val)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutLogistic(idx_val, idx_val)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutLogistic(idx_val, idx_val)
algo = Implicit()
val_imp, grad_imp = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
assert np.allclose(val_fwd, val_imp_fwd, atol=1e-4)
assert np.allclose(grad_fwd, grad_imp_fwd, atol=1e-4)
assert np.allclose(val_imp_fwd, val_imp, atol=1e-4)
# for the implcit the conjugate grad does not converge
# hence the rtol=1e-2
assert np.allclose(grad_imp_fwd, grad_imp, rtol=1e-2)
def test_grad_search(model, crit):
"""check that the paths are the same in the line search"""
if crit == 'cv':
n_outer = 2
criterion = HeldOutMSE(idx_train, idx_val)
else:
n_outer = 2
criterion = SmoothedSURE(sigma_star)
# TODO MM@QBE if else scheme surprising
criterion = HeldOutMSE(idx_train, idx_val)
monitor1 = Monitor()
algo = Forward()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor1,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(idx_train, idx_val)
monitor2 = Monitor()
algo = Implicit()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor2,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(idx_train, idx_val)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor3,
n_outer=n_outer,
tol=1e-16)
np.testing.assert_allclose(np.array(monitor1.log_alphas),
np.array(monitor3.log_alphas))
np.testing.assert_allclose(np.array(monitor1.grads),
np.array(monitor3.grads),
atol=1e-8)
np.testing.assert_allclose(np.array(monitor1.objs),
np.array(monitor3.objs))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
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_grad_search_custom(model, model_custom, crit):
"""check that the paths are the same in the line search"""
n_outer = 5
criterion = HeldOutLogistic(idx_val, idx_val)
monitor = Monitor()
algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000)
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor,
n_outer=n_outer,
tol=tol)
criterion = HeldOutLogistic(idx_val, idx_val)
monitor_custom = Monitor()
algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000)
grad_search(algo,
criterion,
model_custom,
X,
y,
log_alpha,
monitor_custom,
n_outer=n_outer,
tol=tol)
np.testing.assert_allclose(np.array(monitor.log_alphas),
np.array(monitor_custom.log_alphas),
atol=1e-3)
np.testing.assert_allclose(np.array(monitor.grads),
np.array(monitor_custom.grads),
atol=1e-4)
np.testing.assert_allclose(np.array(monitor.objs),
np.array(monitor_custom.objs),
atol=1e-5)
assert not np.allclose(np.array(monitor.times),
np.array(monitor_custom.times))
def test_val_grad_custom(model, model_custom):
criterion = HeldOutLogistic(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutLogistic(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_custom, grad_custom = criterion.get_val_grad(model_custom,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
assert np.allclose(val, val_custom)
assert np.allclose(grad, grad_custom)
def test_grad_search(model, crit):
"""check that the paths are the same in the line search"""
n_outer = 2
criterion = HeldOutLogistic(idx_val, idx_val)
monitor1 = Monitor()
algo = Forward()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor1,
n_outer=n_outer,
tol=tol)
criterion = HeldOutLogistic(idx_val, idx_val)
monitor2 = Monitor()
algo = Implicit()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor2,
n_outer=n_outer,
tol=tol)
criterion = HeldOutLogistic(idx_val, idx_val)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=tol, n_iter_jac=5000)
grad_search(algo,
criterion,
model,
X,
y,
log_alpha,
monitor3,
n_outer=n_outer,
tol=tol)
assert np.allclose(np.array(monitor1.log_alphas),
np.array(monitor3.log_alphas))
assert np.allclose(np.array(monitor1.grads),
np.array(monitor3.grads),
atol=1e-4)
assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
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))
def test_grad_search(model, crit):
"""check that the paths are the same in the line search"""
if crit == 'MSE':
n_outer = 2
criterion = HeldOutMSE(idx_train, idx_val)
else:
n_outer = 2
criterion = FiniteDiffMonteCarloSure(sigma_star)
# TODO MM@QBE if else scheme surprising
criterion = HeldOutMSE(idx_train, idx_val)
monitor1 = Monitor()
algo = Forward()
optimizer = LineSearch(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor1)
criterion = HeldOutMSE(idx_train, idx_val)
monitor2 = Monitor()
algo = Implicit()
optimizer = LineSearch(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor2)
criterion = HeldOutMSE(idx_train, idx_val)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
optimizer = LineSearch(n_outer=n_outer, tol=1e-16)
grad_search(algo, criterion, model, optimizer, X, y, alpha0, monitor3)
np.testing.assert_allclose(np.array(monitor1.alphas),
np.array(monitor3.alphas))
np.testing.assert_allclose(np.array(monitor1.grads),
np.array(monitor3.grads),
rtol=1e-5)
np.testing.assert_allclose(np.array(monitor1.objs),
np.array(monitor3.objs))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
##############################################################################
# Grad-search
# -----------
print('sparse-ho started')
t0 = time.time()
estimator = LogisticRegression(penalty='l1',
fit_intercept=False,
solver='saga',
tol=tol)
model = SparseLogreg(max_iter=max_iter, estimator=estimator)
criterion = HeldOutLogistic(idx_train, idx_val)
monitor_grad = Monitor()
algo = ImplicitForward(tol_jac=tol, n_iter_jac=1000)
grad_search(algo,
criterion,
model,
X,
y,
np.log(0.1 * alpha_max),
monitor_grad,
n_outer=10,
tol=tol)
objs_grad = np.array(monitor_grad.objs)
t_grad_search = time.time() - t0
print('sparse-ho finished')
print("Time to compute CV for sparse-ho: %.2f" % t_grad_search)
def parallel_function(dataset_name,
method,
tol=1e-5,
n_outer=50,
tolerance_decrease='constant'):
# load data
X, y = fetch_libsvm(dataset_name)
y -= np.mean(y)
# compute alpha_max
alpha_max = np.abs(X.T @ y).max() / len(y)
if model_name == "logreg":
alpha_max /= 2
alpha_min = alpha_max * dict_palphamin[dataset_name]
if model_name == "enet":
estimator = linear_model.ElasticNet(fit_intercept=False,
max_iter=10_000,
warm_start=True,
tol=tol)
model = ElasticNet(estimator=estimator)
elif model_name == "logreg":
model = SparseLogreg(estimator=estimator)
# TODO improve this
try:
n_outer = dict_n_outers[dataset_name, method]
except Exception:
n_outer = 20
size_loop = 2
for _ in range(size_loop):
if model_name == "lasso" or model_name == "enet":
sub_criterion = HeldOutMSE(None, None)
elif model_name == "logreg":
criterion = HeldOutLogistic(None, None)
kf = KFold(n_splits=5, shuffle=True, random_state=42)
criterion = CrossVal(sub_criterion, cv=kf)
algo = ImplicitForward(tol_jac=1e-3)
monitor = Monitor()
t_max = dict_t_max[dataset_name]
if method == 'grid_search':
num1D = dict_point_grid_search[dataset_name]
alpha1D = np.geomspace(alpha_max, alpha_min, num=num1D)
alphas = [np.array(i) for i in product(alpha1D, alpha1D)]
grid_search(algo,
criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor,
max_evals=100,
tol=tol,
alphas=alphas)
elif method == 'random' or method == 'bayesian':
hyperopt_wrapper(algo,
criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor,
max_evals=30,
tol=tol,
method=method,
size_space=2,
t_max=t_max)
elif method.startswith("implicit_forward"):
# do gradient descent to find the optimal lambda
alpha0 = np.array([alpha_max / 100, alpha_max / 100])
n_outer = 30
if method == 'implicit_forward':
optimizer = GradientDescent(n_outer=n_outer,
p_grad_norm=1,
verbose=True,
tol=tol,
t_max=t_max)
else:
optimizer = GradientDescent(n_outer=n_outer,
p_grad_norm=1,
verbose=True,
tol=tol,
t_max=t_max,
tol_decrease="geom")
grad_search(algo, criterion, model, optimizer, X, y, alpha0,
monitor)
else:
raise NotImplementedError
monitor.times = np.array(monitor.times)
monitor.objs = np.array(monitor.objs)
monitor.objs_test = 0 # TODO
monitor.alphas = np.array(monitor.alphas)
return (dataset_name, method, tol, n_outer, tolerance_decrease,
monitor.times, monitor.objs, monitor.objs_test, monitor.alphas,
alpha_max, model_name)
n_jobs=2).fit(X, y)
# Measure mse on test
mse_cv = mean_squared_error(y_test, model_cv.predict(X_test))
print("Vanilla LassoCV: Mean-squared error on test data %f" % mse_cv)
##############################################################################
##############################################################################
# Weighted Lasso with sparse-ho.
# We use the vanilla lassoCV coefficients as a starting point
log_alpha0 = np.log(model_cv.alpha_) * np.ones(n_features)
# Weighted Lasso: Sparse-ho: 1 param per feature
estimator = Lasso(fit_intercept=False, max_iter=10, warm_start=True)
model = WeightedLasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward()
monitor = Monitor()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha0,
monitor,
n_outer=20,
tol=1e-6)
##############################################################################
##############################################################################
# MSE on validation set
mse_sho_val = mean_squared_error(y[idx_val], estimator.predict(X[idx_val, :]))
def parallel_function(dataset_name,
div_alpha,
method,
ind_rep,
random_state=10):
maxit = dict_maxits[(dataset_name, div_alpha)][ind_rep]
print("Dataset %s, algo %s, maxit %i" % (dataset_name, method, maxit))
X, y = fetch_libsvm(dataset_name)
n_samples = len(y)
kf = KFold(n_splits=5, random_state=random_state, shuffle=True)
for i in range(2):
alpha_max = np.max(np.abs(X.T.dot(y))) / n_samples
log_alpha = np.log(alpha_max / div_alpha)
monitor = Monitor()
if method == "celer":
clf = Lasso_celer(
alpha=np.exp(log_alpha),
fit_intercept=False,
# TODO maybe change this tol
tol=1e-8,
max_iter=maxit)
model = Lasso(estimator=clf, max_iter=maxit)
criterion = HeldOutMSE(None, None)
cross_val = CrossVal(cv=kf, criterion=criterion)
algo = ImplicitForward(tol_jac=1e-8,
n_iter_jac=maxit,
use_stop_crit=False)
algo.max_iter = maxit
val, grad = cross_val.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol,
monitor=monitor,
max_iter=maxit)
elif method == "ground_truth":
for file in os.listdir("results/"):
if file.startswith("hypergradient_%s_%i_%s" %
(dataset_name, div_alpha, method)):
return
else:
clf = Lasso_celer(alpha=np.exp(log_alpha),
fit_intercept=False,
warm_start=True,
tol=1e-13,
max_iter=10000)
criterion = HeldOutMSE(None, None)
cross_val = CrossVal(cv=kf, criterion=criterion)
algo = Implicit(criterion)
model = Lasso(estimator=clf, max_iter=10000)
val, grad = cross_val.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=1e-13,
monitor=monitor)
else:
model = Lasso(max_iter=maxit)
criterion = HeldOutMSE(None, None)
cross_val = CrossVal(cv=kf, criterion=criterion)
if method == "forward":
algo = Forward(use_stop_crit=False)
elif method == "implicit_forward":
algo = ImplicitForward(use_stop_crit=False,
tol_jac=1e-8,
n_iter_jac=maxit,
max_iter=1000)
elif method == "implicit":
algo = Implicit(use_stop_crit=False, max_iter=1000)
elif method == "backward":
algo = Backward()
else:
1 / 0
algo.max_iter = maxit
algo.use_stop_crit = False
val, grad = cross_val.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol,
monitor=monitor,
max_iter=maxit)
results = (dataset_name, div_alpha, method, maxit, val, grad,
monitor.times[0])
df = pandas.DataFrame(results).transpose()
df.columns = [
'dataset', 'div_alpha', 'method', 'maxit', 'val', 'grad', 'time'
]
str_results = "results/hypergradient_%s_%i_%s_%i.pkl" % (
dataset_name, div_alpha, method, maxit)
df.to_pickle(str_results)
def parallel_function(dataset_name, method):
X, y = fetch_libsvm(dataset_name)
X, y = fetch_libsvm(dataset_name)
if dataset_name == "real-sim":
X = X[:, :2000]
X = csr_matrix(X) # very important for SVM
my_bool = norm(X, axis=1) != 0
X = X[my_bool, :]
y = y[my_bool]
logC = dict_logC[dataset_name]
for max_iter in dict_max_iter[dataset_name]:
print("Dataset %s, max iter %i" % (method, max_iter))
for i in range(2): # TODO change this
sss1 = StratifiedShuffleSplit(n_splits=2,
test_size=0.3333,
random_state=0)
idx_train, idx_val = sss1.split(X, y)
idx_train = idx_train[0]
idx_val = idx_val[0]
monitor = Monitor()
criterion = HeldOutSmoothedHinge(idx_train, idx_val)
model = SVM(estimator=None, max_iter=10_000)
if method == "ground_truth":
for file in os.listdir("results_svm/"):
if file.startswith("hypergradient_svm_%s_%s" %
(dataset_name, method)):
return
clf = LinearSVC(C=np.exp(logC),
tol=1e-32,
max_iter=10_000,
loss='hinge',
permute=False)
algo = Implicit(criterion)
model.estimator = clf
val, grad = criterion.get_val_grad(model,
X,
y,
logC,
algo.compute_beta_grad,
tol=1e-14,
monitor=monitor)
else:
if method == "sota":
clf = LinearSVC(C=np.exp(logC),
loss='hinge',
max_iter=max_iter,
tol=1e-32,
permute=False)
model.estimator = clf
algo = ImplicitForward(tol_jac=1e-32,
n_iter_jac=max_iter,
use_stop_crit=False)
elif method == "forward":
algo = Forward(use_stop_crit=False)
elif method == "implicit_forward":
algo = ImplicitForward(tol_jac=1e-8,
n_iter_jac=max_iter,
use_stop_crit=False)
else:
raise NotImplementedError
algo.max_iter = max_iter
algo.use_stop_crit = False
val, grad = criterion.get_val_grad(model,
X,
y,
logC,
algo.compute_beta_grad,
tol=tol,
monitor=monitor,
max_iter=max_iter)
results = (dataset_name, method, max_iter, val, grad, monitor.times[0])
df = pandas.DataFrame(results).transpose()
df.columns = ['dataset', 'method', 'maxit', 'val', 'grad', 'time']
str_results = "results_svm/hypergradient_svm_%s_%s_%i.pkl" % (
dataset_name, method, max_iter)
df.to_pickle(str_results)
tol=tol)
objs = np.array(monitor_grid_sk.objs)
t_sk = time.time() - t0
print('scikit-learn finished')
##############################################################################
# Grad-search with sparse-ho
# --------------------------
print('sparse-ho started')
t0 = time.time()
model = Lasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(criterion)
monitor_grad = Monitor()
grad_search(algo,
criterion,
model,
X,
y,
np.log(alpha_max / 10),
monitor_grad,
n_outer=10,
tol=tol)
t_grad_search = time.time() - t0
print('sparse-ho finished')
dict_monitor = {}
all_algo_name = ['grid_search']
# , 'implicit_forward', "implicit_forward_approx", 'bayesian']
# , 'random_search']
# all_algo_name = ['random_search']
for algo_name in all_algo_name:
model = ElasticNet(estimator=estimator)
sub_criterion = HeldOutMSE(None, None)
alpha0 = np.array([alpha_max / 10, alpha_max / 10])
monitor = Monitor()
kf = KFold(n_splits=5, shuffle=True, random_state=42)
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':
t_grid_search += time.time()
print("Finished grid-search")
##############################################################################
# Grad-search with sparse-ho
# --------------------------
estimator = linear_model.ElasticNet(fit_intercept=False,
max_iter=max_iter,
warm_start=True)
print("Started grad-search")
t_grad_search = -time.time()
monitor = Monitor()
n_outer = 10
model = ElasticNet(max_iter=max_iter, estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-7, n_iter_jac=1000, max_iter=max_iter)
grad_search(algo,
criterion,
model,
X,
y,
verbose=True,
log_alpha0=np.array(
[np.log(alpha_max * 0.3),
np.log(alpha_max / 10)]),
tol=tol,
n_outer=n_outer,
monitor=monitor)
t_grad_search += time.time()
alphas_grad = np.exp(np.array(monitor.log_alphas))
alphas_grad /= alpha_max
from sparse_ho.algo.forward import get_beta_jac_iterdiff
from sparse_ho.algo.implicit_forward import get_beta_jac_fast_iterdiff
from sparse_ho.algo.implicit import get_beta_jac_t_v_implicit
from sparse_ho.criterion import (
HeldOutMSE, FiniteDiffMonteCarloSure, HeldOutLogistic)
from sparse_ho.tests.common import (
X, X_s, y, sigma_star, idx_train, idx_val,
dict_log_alpha, models, custom_models, dict_cvxpy_func,
dict_vals_cvxpy, dict_grads_cvxpy, dict_list_log_alphas, get_v,
list_model_crit, list_model_names)
# list of algorithms to be tested
list_algos = [
Forward(),
ImplicitForward(tol_jac=1e-16, n_iter_jac=5000),
Implicit()
# Backward() # XXX to fix
]
tol = 1e-15
X_r = X_s.tocsr()
X_c = X_s
@pytest.mark.parametrize('key', list(models.keys()))
def test_beta_jac(key):
"""Tests that algorithms computing the Jacobian return the same Jacobian"""
if key == "svm" or key == "svr" or key == "ssvr":
X_s = X_r
else:
for maxit in maxits:
for method in methods:
print("Dataset %s, maxit %i" % (method, maxit))
for i in range(2):
alpha_max = np.max(np.abs(X.T.dot(y))) / n_samples
log_alpha = np.log(alpha_max * p_alpha_max)
monitor = Monitor()
if method == "celer":
clf = Lasso_celer(alpha=np.exp(log_alpha),
fit_intercept=False,
tol=1e-12,
max_iter=maxit)
model = Lasso(estimator=clf, max_iter=maxit)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-32,
n_iter_jac=maxit,
use_stop_crit=False)
algo.max_iter = maxit
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=1e-12,
monitor=monitor,
max_iter=maxit)
else:
model = Lasso(max_iter=maxit)
criterion = HeldOutMSE(idx_train, idx_val)
if method == "forward":
algo = Forward()
def parallel_function(dataset_name, div_alpha, method):
X, y = fetch_libsvm(dataset_name)
n_samples = len(y)
if dataset_name == "news20" and div_alpha == 100:
rng = np.random.RandomState(42)
y += rng.randn(n_samples) * 0.01
for maxit in dict_maxits[(dataset_name, div_alpha)]:
print("Dataset %s, maxit %i" % (method, maxit))
for i in range(2):
rng = np.random.RandomState(i)
idx_train = rng.choice(n_samples, n_samples // 2, replace=False)
idx = np.arange(0, n_samples)
idx_val = idx[np.logical_not(np.isin(idx, idx_train))]
alpha_max = np.max(np.abs(X[idx_train, :].T.dot(y[idx_train])))
alpha_max /= len(idx_train)
log_alpha = np.log(alpha_max / div_alpha)
monitor = Monitor()
if method == "celer":
clf = Lasso_celer(alpha=np.exp(log_alpha),
fit_intercept=False,
tol=1e-12,
max_iter=maxit)
model = Lasso(estimator=clf, max_iter=maxit)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-32,
n_iter_jac=maxit,
use_stop_crit=False)
algo.max_iter = maxit
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.compute_beta_grad,
tol=1e-12,
monitor=monitor,
max_iter=maxit)
elif method == "ground_truth":
for file in os.listdir("results/"):
if file.startswith("hypergradient_%s_%i_%s" %
(dataset_name, div_alpha, method)):
return
clf = Lasso_celer(alpha=np.exp(log_alpha),
fit_intercept=False,
warm_start=True,
tol=1e-14,
max_iter=10000)
criterion = HeldOutMSE(idx_train, idx_val)
if dataset_name == "news20":
algo = ImplicitForward(tol_jac=1e-11, n_iter_jac=100000)
else:
algo = Implicit(criterion)
model = Lasso(estimator=clf, max_iter=10000)
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.compute_beta_grad,
tol=1e-14,
monitor=monitor)
else:
model = Lasso(max_iter=maxit)
criterion = HeldOutMSE(idx_train, idx_val)
if method == "forward":
algo = Forward(use_stop_crit=False)
elif method == "implicit_forward":
algo = ImplicitForward(tol_jac=1e-8,
n_iter_jac=maxit,
use_stop_crit=False)
elif method == "implicit":
algo = Implicit(max_iter=1000)
elif method == "backward":
algo = Backward()
else:
raise NotImplementedError
algo.max_iter = maxit
algo.use_stop_crit = False
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.compute_beta_grad,
tol=tol,
monitor=monitor,
max_iter=maxit)
results = (dataset_name, div_alpha, method, maxit, val, grad,
monitor.times[0])
df = pandas.DataFrame(results).transpose()
df.columns = [
'dataset', 'div_alpha', 'method', 'maxit', 'val', 'grad', 'time'
]
str_results = "results/hypergradient_%s_%i_%s_%i.pkl" % (
dataset_name, div_alpha, method, maxit)
df.to_pickle(str_results)
def test_val_grad():
#######################################################################
# Not all methods computes the full Jacobian, but all
# compute the gradients
# check that the gradient returned by all methods are the same
for key in models.keys():
log_alpha = dict_log_alpha[key]
model = models[key]
criterion = HeldOutMSE(idx_train, idx_val)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = Implicit()
val_imp, grad_imp = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(idx_train, idx_val)
algo = Backward()
val_bwd, grad_bwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
assert np.allclose(val_fwd, val_imp_fwd)
assert np.allclose(grad_fwd, grad_imp_fwd)
# assert np.allclose(val_imp_fwd, val_imp)
assert np.allclose(val_bwd, val_fwd)
assert np.allclose(val_bwd, val_imp_fwd)
assert np.allclose(grad_fwd, grad_bwd)
assert np.allclose(grad_bwd, grad_imp_fwd)
# for the implcit the conjugate grad does not converge
# hence the rtol=1e-2
assert np.allclose(grad_imp_fwd, grad_imp, atol=1e-3)
for key in models.keys():
log_alpha = dict_log_alpha[key]
model = models[key]
criterion = SmoothedSURE(sigma_star)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SmoothedSURE(sigma_star)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SmoothedSURE(sigma_star)
algo = Implicit(criterion)
val_imp, grad_imp = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SmoothedSURE(sigma_star)
algo = Backward()
val_bwd, grad_bwd = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.get_beta_jac_v,
tol=tol)
assert np.allclose(val_fwd, val_imp_fwd)
assert np.allclose(grad_fwd, grad_imp_fwd)
assert np.allclose(val_imp_fwd, val_imp)
assert np.allclose(val_bwd, val_fwd)
assert np.allclose(val_bwd, val_imp_fwd)
assert np.allclose(grad_fwd, grad_bwd)
assert np.allclose(grad_bwd, grad_imp_fwd)
# n_features = 3_200
# X, y = fetch_libsvm('sensit')
# X, y = fetch_libsvm('usps')
X, y = fetch_libsvm('rcv1_multiclass')
# X, y = fetch_libsvm('sector_scale')
# X, y = fetch_libsvm('sector')
# X, y = fetch_libsvm('smallNORB')
# X, y = fetch_libsvm('mnist')
# clean data and subsample
X, y = clean_dataset(X, y, n_samples, n_features)
idx_train, idx_val, idx_test = get_splits(X, y)
n_samples, n_features = X.shape
algo = ImplicitForward(n_iter_jac=1000)
estimator = LogisticRegression(
C=1, fit_intercept=False, warm_start=True, max_iter=2000, verbose=False)
model = SparseLogreg(estimator=estimator)
logit_multiclass = LogisticMulticlass(
idx_train, idx_val, algo, idx_test=idx_test)
alpha_max, n_classes = alpha_max_multiclass(X, y)
tol = 1e-5
n_alphas = 10
p_alphas = np.geomspace(1, 0.001, n_alphas)
p_alphas = np.tile(p_alphas, (n_classes, 1))
def test_grad_search():
n_outer = 3
criterion = HeldOutMSE(idx_train, idx_val)
monitor1 = Monitor()
algo = Forward()
grad_search(algo,
criterion,
model,
X,
y,
np.array([log_alpha1, log_alpha2]),
monitor1,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(idx_train, idx_val)
monitor2 = Monitor()
algo = Implicit()
grad_search(algo,
criterion,
model,
X,
y,
np.array([log_alpha1, log_alpha2]),
monitor2,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(idx_train, idx_val)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-3, n_iter_jac=1000)
grad_search(algo,
criterion,
model,
X,
y,
np.array([log_alpha1, log_alpha2]),
monitor3,
n_outer=n_outer,
tol=1e-16)
[np.linalg.norm(grad) for grad in monitor1.grads]
[np.exp(alpha) for alpha in monitor1.log_alphas]
np.testing.assert_allclose(np.array(monitor1.log_alphas),
np.array(monitor3.log_alphas))
np.testing.assert_allclose(np.array(monitor1.grads),
np.array(monitor3.grads),
rtol=1e-6)
np.testing.assert_allclose(np.array(monitor1.objs),
np.array(monitor3.objs),
rtol=1e-6)
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
np.testing.assert_allclose(np.array(monitor1.log_alphas),
np.array(monitor2.log_alphas),
atol=1e-2)
np.testing.assert_allclose(np.array(monitor1.grads),
np.array(monitor2.grads),
atol=1e-2)
np.testing.assert_allclose(np.array(monitor1.objs),
np.array(monitor2.objs),
atol=1e-2)
assert not np.allclose(np.array(monitor1.times), np.array(monitor2.times))