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_name, criterion_name, algo):
"""Check that all methods return the same gradient, comparing to cvxpylayer
"""
if model_name == 'svr':
pytest.xfail("svr needs to be fixed")
if criterion_name == 'logistic':
pytest.xfail("cvxpylayer seems broken for logistic")
if criterion_name == 'MSE':
criterion = HeldOutMSE(idx_train, idx_val)
elif criterion_name == 'logistic':
criterion = HeldOutLogistic(idx_train, idx_val)
elif criterion_name == 'SURE':
criterion = FiniteDiffMonteCarloSure(sigma_star)
log_alpha = dict_log_alpha[model_name]
model = models[model_name]
val, grad = criterion.get_val_grad(model,
X,
y,
log_alpha,
algo.compute_beta_grad,
tol=tol)
np.testing.assert_allclose(dict_vals_cvxpy[model_name, criterion_name],
val,
rtol=1e-5,
atol=1e-5)
np.testing.assert_allclose(dict_grads_cvxpy[model_name, criterion_name],
grad,
rtol=1e-5,
atol=1e-5)
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_grad_search(model, crit):
"""check that the paths are the same in the line search"""
if crit == 'cv':
n_outer = 2
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
else:
n_outer = 2
criterion = SURE(X_train,
y_train,
model,
sigma=sigma_star,
X_test=X_test,
y_test=y_test)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
monitor1 = Monitor()
algo = Forward()
grad_search(algo,
criterion,
log_alpha,
monitor1,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
monitor2 = Monitor()
algo = Implicit()
grad_search(algo,
criterion,
log_alpha,
monitor2,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
grad_search(algo,
criterion,
log_alpha,
monitor3,
n_outer=n_outer,
tol=1e-16)
assert np.allclose(np.array(monitor1.log_alphas),
np.array(monitor3.log_alphas))
assert np.allclose(np.array(monitor1.grads), np.array(monitor3.grads))
assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs))
assert np.allclose(np.array(monitor1.objs_test),
np.array(monitor3.objs_test))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
def test_grad_search():
n_outer = 3
criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None)
monitor1 = Monitor()
algo = Forward()
grad_search(algo,
criterion,
np.array([log_alpha1, log_alpha2]),
monitor1,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None)
monitor2 = Monitor()
algo = Implicit()
grad_search(algo,
criterion,
np.array([log_alpha1, log_alpha2]),
monitor2,
n_outer=n_outer,
tol=1e-16)
criterion = HeldOutMSE(X_val, y_val, model, X_test=None, y_test=None)
monitor3 = Monitor()
algo = ImplicitForward(tol_jac=1e-3, n_iter_jac=1000)
grad_search(algo,
criterion,
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]
assert np.allclose(np.array(monitor1.log_alphas),
np.array(monitor3.log_alphas))
assert np.allclose(np.array(monitor1.grads), np.array(monitor3.grads))
assert np.allclose(np.array(monitor1.objs), np.array(monitor3.objs))
assert not np.allclose(np.array(monitor1.times), np.array(monitor3.times))
assert np.allclose(np.array(monitor1.log_alphas),
np.array(monitor2.log_alphas),
atol=1e-2)
assert np.allclose(np.array(monitor1.grads),
np.array(monitor2.grads),
atol=1e-2)
assert np.allclose(np.array(monitor1.objs),
np.array(monitor2.objs),
atol=1e-2)
assert not np.allclose(np.array(monitor1.times), np.array(monitor2.times))
def test_check_grad_sparse_ho(model_name, criterion, algo):
"""Check that all methods return a good gradient using check_grad"""
if criterion == 'MSE':
criterion = HeldOutMSE(idx_train, idx_val)
elif criterion == 'SURE':
criterion = FiniteDiffMonteCarloSure(sigma_star)
elif criterion == 'logistic':
criterion = HeldOutLogistic(idx_train, idx_val)
model = models[model_name]
log_alpha = dict_log_alpha[model_name]
def get_val(log_alpha):
val, _ = criterion.get_val_grad(
model, X, y, np.squeeze(log_alpha), algo.get_beta_jac_v, tol=tol)
return val
def get_grad(log_alpha):
_, grad = criterion.get_val_grad(
model, X, y, np.squeeze(log_alpha), algo.get_beta_jac_v, tol=tol)
return grad
for log_alpha in dict_list_log_alphas[model_name]:
grad_error = check_grad(get_val, get_grad, log_alpha)
assert grad_error < 1e-1
def test_grid_search():
max_evals = 5
monitor_grid = Monitor()
model = Lasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_train)
algo = Forward()
log_alpha_opt_grid, _ = grid_search(
algo, criterion, model, X, y, log_alpha_min, log_alpha_max,
monitor_grid, max_evals=max_evals,
tol=1e-5, samp="grid")
monitor_random = Monitor()
criterion = HeldOutMSE(idx_train, idx_val)
algo = Forward()
log_alpha_opt_random, _ = grid_search(
algo, criterion, model, X, y, log_alpha_min, log_alpha_max,
monitor_random,
max_evals=max_evals, tol=1e-5, samp="random")
assert(monitor_random.log_alphas[
np.argmin(monitor_random.objs)] == log_alpha_opt_random)
assert(monitor_grid.log_alphas[
np.argmin(monitor_grid.objs)] == log_alpha_opt_grid)
monitor_grid = Monitor()
model = Lasso(estimator=estimator)
criterion = SmoothedSURE(sigma=sigma_star)
algo = Forward()
log_alpha_opt_grid, _ = grid_search(
algo, criterion, model, X, y, log_alpha_min, log_alpha_max,
monitor_grid, max_evals=max_evals,
tol=1e-5, samp="grid")
monitor_random = Monitor()
criterion = SmoothedSURE(sigma=sigma_star)
algo = Forward()
log_alpha_opt_random, _ = grid_search(
algo, criterion, model, X, y, log_alpha_min, log_alpha_max,
monitor_random,
max_evals=max_evals, tol=1e-5, samp="random")
assert(monitor_random.log_alphas[
np.argmin(monitor_random.objs)] == log_alpha_opt_random)
assert(monitor_grid.log_alphas[
np.argmin(monitor_grid.objs)] == log_alpha_opt_grid)
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_cross_val_criterion(model_name, XX):
model = models[model_name]
alpha_min = alpha_max / 10
max_iter = 10000
n_alphas = 10
kf = KFold(n_splits=5, shuffle=True, random_state=56)
monitor_grid = Monitor()
if model_name.startswith("lasso"):
sub_crit = HeldOutMSE(None, None)
else:
sub_crit = HeldOutLogistic(None, None)
criterion = CrossVal(sub_crit, cv=kf)
grid_search(criterion,
model,
XX,
y,
alpha_min,
alpha_max,
monitor_grid,
max_evals=n_alphas,
tol=tol)
if model_name.startswith("lasso"):
reg = linear_model.LassoCV(cv=kf,
verbose=True,
tol=tol,
fit_intercept=False,
alphas=np.geomspace(alpha_max,
alpha_min,
num=n_alphas),
max_iter=max_iter).fit(X, y)
else:
reg = linear_model.LogisticRegressionCV(
cv=kf,
verbose=True,
tol=tol,
fit_intercept=False,
Cs=len(idx_train) /
np.geomspace(alpha_max, alpha_min, num=n_alphas),
max_iter=max_iter,
penalty='l1',
solver='liblinear').fit(X, y)
reg.score(XX, y)
if model_name.startswith("lasso"):
objs_grid_sk = reg.mse_path_.mean(axis=1)
else:
objs_grid_sk = reg.scores_[1.0].mean(axis=1)
# these 2 value should be the same
(objs_grid_sk - np.array(monitor_grid.objs))
np.testing.assert_allclose(objs_grid_sk, monitor_grid.objs)
def __init__(self, X, y, Model, cv=None, max_iter=1000, estimator=None):
"""
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
Data
y : {ndarray, sparse matrix} of shape (n_samples,)
Target
Model: class
The Model class definition (e.g. Lasso or SparseLogreg)
cv : int, cross-validation generator or iterable, default=None
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- int, to specify the number of folds.
- scikit-learn CV splitter
- An iterable yielding (train, test) splits as arrays of indices.
For int/None inputs, KFold is used.
max_iter: int
Maximal number of iteration for the state-of-the-art solver
estimator: instance of ``sklearn.base.BaseEstimator``
An estimator that follows the scikit-learn API.
"""
self.X = X
self.y = y
self.dict_crits = {}
self.dict_models = {}
self.rmse = None
self.estimator = estimator
cv = check_cv(cv)
for i, (idx_train, idx_val) in enumerate(cv.split(X)):
X_train = X[idx_train, :]
y_train = y[idx_train]
if issparse(X_train):
X_train = X_train.tocsc()
# TODO get rid of this
self.models[i] = Model(X_train,
y_train,
max_iter=max_iter,
estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_val)
self.dict_crits[i] = criterion
self.n_splits = cv.n_splits
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))
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_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(X_val, y_val, model)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(np.array(
[log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(X_val, y_val, model)
algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(np.array(
[log_alpha1, log_alpha2]),
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(X_val, y_val, model)
algo = ImplicitForward(tol_jac=1e-16, n_iter_jac=5000)
val_imp_fwd_custom, grad_imp_fwd_custom = criterion.get_val_grad(
np.array([log_alpha1, log_alpha2]), algo.get_beta_jac_v, tol=tol)
criterion = HeldOutMSE(X_val, y_val, model)
algo = Implicit()
val_imp, grad_imp = criterion.get_val_grad(np.array(
[log_alpha1, log_alpha2]),
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_imp_fwd, val_imp_fwd_custom)
# 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)
assert np.allclose(grad_imp_fwd, grad_imp_fwd_custom)
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)
import pytest
import numpy as np
from sparse_ho.criterion import (HeldOutMSE, HeldOutLogistic,
FiniteDiffMonteCarloSure)
from sparse_ho.utils import Monitor
from sparse_ho import Forward
from sparse_ho.tests.common import (X, y, sigma_star, idx_train, idx_val,
models, dict_list_log_alphas)
list_model_crit = [('lasso', HeldOutMSE(idx_train, idx_val)),
('enet', HeldOutMSE(idx_train, idx_val)),
('wLasso', HeldOutMSE(idx_train, idx_val)),
('lasso', FiniteDiffMonteCarloSure(sigma_star)),
('logreg', HeldOutLogistic(idx_train, idx_val))]
tol = 1e-15
@pytest.mark.parametrize('model_name,criterion', list_model_crit)
def test_cross_val_criterion(model_name, criterion):
# verify dtype from criterion, and the good shape
algo = Forward()
monitor_get_val = Monitor()
monitor_get_val_grad = Monitor()
model = models[model_name]
for log_alpha in dict_list_log_alphas[model_name]:
criterion.get_val(model,
X,
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():
# model = Lasso(log_alpha)
log_alpha = dict_log_alpha[key]
model = models[key]
# model = Lasso(log_alpha)
criterion = HeldOutMSE(X_val, y_val, model)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(X_val, y_val, model)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = HeldOutMSE(X_val, y_val, model)
algo = Implicit()
val_imp, grad_imp = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
# import ipdb; ipdb.set_trace()
criterion = HeldOutMSE(X_val, y_val, model)
algo = Backward()
val_bwd, grad_bwd = criterion.get_val_grad(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():
# model = Lasso(log_alpha)
log_alpha = dict_log_alpha[key]
model = models[key]
# model = Lasso(log_alpha)
criterion = SURE(X_train, y_train, model, sigma_star)
algo = Forward()
val_fwd, grad_fwd = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SURE(X_train, y_train, model, sigma_star)
algo = ImplicitForward(tol_jac=1e-8, n_iter_jac=5000)
val_imp_fwd, grad_imp_fwd = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SURE(X_train, y_train, model, sigma_star)
algo = Implicit(criterion)
val_imp, grad_imp = criterion.get_val_grad(log_alpha,
algo.get_beta_jac_v,
tol=tol)
criterion = SURE(X_train, y_train, model, sigma_star)
algo = Backward()
val_bwd, grad_bwd = criterion.get_val_grad(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)
cv=2,
n_jobs=2).fit(X_train_val, y_train_val)
# 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
alpha0 = np.log(model_cv.alpha_) * np.ones(X_train.shape[1])
# Weighted Lasso: Sparse-ho: 1 param per feature
estimator = Lasso(fit_intercept=False, max_iter=10, warm_start=True)
model = WeightedLasso(X_train, y_train, estimator=estimator)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = ImplicitForward()
monitor = Monitor()
grad_search(algo, criterion, alpha0, monitor, n_outer=20, tol=1e-6)
##############################################################################
##############################################################################
# MSE on validation set
mse_sho_val = mean_squared_error(y_val, estimator.predict(X_val))
# MSE on test set, ie unseen data
mse_sho_test = mean_squared_error(y_test, estimator.predict(X_test))
print("Sparse-ho: Mean-squared error on validation data %f" % mse_sho_val)
print("Sparse-ho: Mean-squared error on test (unseen) data %f" % mse_sho_test)
cv=2,
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
def parallel_function(dataset_name,
method,
tol=1e-5,
n_outer=50,
tolerance_decrease='exponential'):
# load data
X_train, X_val, X_test, y_train, y_val, y_test = get_data(dataset_name)
n_samples, n_features = X_train.shape
print('n_samples', n_samples)
print('n_features', n_features)
y_train[y_train == 0.0] = -1.0
y_val[y_val == 0.0] = -1.0
y_test[y_test == 0.0] = -1.0
alpha_max = np.max(np.abs(X_train.T @ y_train))
alpha_max /= X_train.shape[0]
log_alpha_max = np.log(alpha_max)
alpha_min = alpha_max * 1e-2
# alphas = np.geomspace(alpha_max, alpha_min, 10)
# log_alphas = np.log(alphas)
log_alpha1_0 = np.log(0.1 * alpha_max)
log_alpha2_0 = np.log(0.1 * alpha_max)
log_alpha_max = np.log(alpha_max)
n_outer = 25
if dataset_name == "rcv1":
size_loop = 2
else:
size_loop = 2
model = ElasticNet(X_train,
y_train,
log_alpha1_0,
log_alpha2_0,
log_alpha_max,
max_iter=1000,
tol=tol)
for i in range(size_loop):
monitor = Monitor()
if method == "implicit_forward":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = ImplicitForward(criterion, tol_jac=1e-3, n_iter_jac=100)
_, _, _ = grad_search(algo=algo,
verbose=False,
log_alpha0=np.array(
[log_alpha1_0, log_alpha2_0]),
tol=tol,
n_outer=n_outer,
monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "forward":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = Forward(criterion)
_, _, _ = grad_search(algo=algo,
log_alpha0=np.array(
[log_alpha1_0, log_alpha2_0]),
tol=tol,
n_outer=n_outer,
monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "implicit":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = Implicit(criterion)
_, _, _ = grad_search(algo=algo,
log_alpha0=np.array(
[log_alpha1_0, log_alpha2_0]),
tol=tol,
n_outer=n_outer,
monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "grid_search":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = Forward(criterion)
log_alpha_min = np.log(alpha_min)
log_alpha_opt, min_g_func = grid_search(
algo,
log_alpha_min,
log_alpha_max,
monitor,
max_evals=10,
tol=tol,
samp="grid",
t_max=dict_t_max[dataset_name],
log_alphas=None,
nb_hyperparam=2)
print(log_alpha_opt)
elif method == "random":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = Forward(criterion)
log_alpha_min = np.log(alpha_min)
log_alpha_opt, min_g_func = grid_search(
algo,
log_alpha_min,
np.log(alpha_max),
monitor,
max_evals=10,
tol=tol,
samp="random",
t_max=dict_t_max[dataset_name],
nb_hyperparam=2)
print(log_alpha_opt)
elif method == "lhs":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = Forward(criterion)
log_alpha_min = np.log(alpha_min)
log_alpha_opt, min_g_func = grid_search(
algo,
log_alpha_min,
np.log(alpha_max),
monitor,
max_evals=10,
tol=tol,
samp="lhs",
t_max=dict_t_max[dataset_name])
print(log_alpha_opt)
monitor.times = np.array(monitor.times).copy()
monitor.objs = np.array(monitor.objs).copy()
monitor.objs_test = np.array(monitor.objs_test).copy()
monitor.log_alphas = np.array(monitor.log_alphas).copy()
return (dataset_name, method, tol, n_outer, tolerance_decrease,
monitor.times, monitor.objs, monitor.objs_test, monitor.log_alphas,
norm(y_val), norm(y_test), log_alpha_max)
def parallel_function(name_model, div_alpha):
index_col = np.arange(10)
alpha_max = (np.abs(X[np.ix_(idx_train, index_col)].T
@ y[idx_train])).max() / len(idx_train)
if name_model == "lasso":
log_alpha = np.log(alpha_max / div_alpha)
elif name_model == "enet":
alpha0 = alpha_max / div_alpha
alpha1 = (1 - l1_ratio) * alpha0 / l1_ratio
log_alpha = np.log(np.array([alpha0, alpha1]))
criterion = HeldOutMSE(idx_train, idx_val)
algo = Forward()
monitor = Monitor()
val, grad = criterion.get_val_grad(dict_models[name_model],
X[:, index_col],
y,
log_alpha,
algo.compute_beta_grad,
tol=tol,
monitor=monitor)
criterion = HeldOutMSE(idx_train, idx_val)
algo = Backward()
monitor = Monitor()
val, grad = criterion.get_val_grad(dict_models[name_model],
X[:, index_col],
y,
log_alpha,
algo.compute_beta_grad,
tol=tol,
monitor=monitor)
val_cvxpy, grad_cvxpy = dict_cvxpy[name_model](X[:, index_col], y,
np.exp(log_alpha),
idx_train, idx_val)
list_times_fwd = []
list_times_bwd = []
list_times_cvxpy = []
for n_col in dict_ncols[div_alpha]:
temp_fwd = []
temp_bwd = []
temp_cvxpy = []
for i in range(repeat):
rng = np.random.RandomState(i)
index_col = rng.choice(n_features, n_col, replace=False)
alpha_max = (np.abs(X[np.ix_(idx_train, index_col)].T
@ y[idx_train])).max() / len(idx_train)
if name_model == "lasso":
log_alpha = np.log(alpha_max / div_alpha)
elif name_model == "enet":
alpha0 = alpha_max / div_alpha
alpha1 = (1 - l1_ratio) * alpha0 / l1_ratio
log_alpha = np.log(np.array([alpha0, alpha1]))
criterion = HeldOutMSE(idx_train, idx_val)
algo = Forward()
monitor = Monitor()
val, grad = criterion.get_val_grad(dict_models[name_model],
X[:, index_col],
y,
log_alpha,
algo.compute_beta_grad,
tol=tol,
monitor=monitor)
temp_fwd.append(monitor.times)
criterion = HeldOutMSE(idx_train, idx_val)
algo = Backward()
monitor = Monitor()
val, grad = criterion.get_val_grad(dict_models[name_model],
X[:, index_col],
y,
log_alpha,
algo.compute_beta_grad,
tol=tol,
monitor=monitor)
temp_bwd.append(monitor.times)
t0 = time.time()
val_cvxpy, grad_cvxpy = dict_cvxpy[name_model](X[:, index_col], y,
np.exp(log_alpha),
idx_train, idx_val)
temp_cvxpy.append(time.time() - t0)
print(np.abs(grad - grad_cvxpy * np.exp(log_alpha)))
list_times_fwd.append(np.mean(np.array(temp_fwd)))
list_times_bwd.append(np.mean(np.array(temp_bwd)))
list_times_cvxpy.append(np.mean(np.array(temp_cvxpy)))
np.save("results/times_%s_forward_%s" % (name_model, div_alpha),
list_times_fwd)
np.save("results/times_%s_backward_%s" % (name_model, div_alpha),
list_times_bwd)
np.save("results/times_%s_cvxpy_%s" % (name_model, div_alpha),
list_times_cvxpy)
np.save("results/nfeatures_%s_%s" % (name_model, div_alpha),
dict_ncols[div_alpha])
estimator = linear_model.ElasticNet(fit_intercept=False,
max_iter=1000,
warm_start=True,
tol=tol)
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,
def parallel_function(dataset_name,
method,
tol=1e-5,
n_outer=50,
tolerance_decrease='constant'):
# load data
X_train, X_val, X_test, y_train, y_val, y_test = get_data(dataset_name)
n_samples, _ = X_train.shape
# compute alpha_max
alpha_max = np.abs(X_train.T @ y_train).max() / n_samples
if model_name == "logreg":
alpha_max /= 2
alpha_min = alpha_max / 10_000
log_alpha_max = np.log(alpha_max)
log_alpha_min = np.log(alpha_min)
log_alpha0 = np.log(0.1 * alpha_max)
if model_name == "lasso":
model = Lasso(X_train, y_train)
elif model_name == "logreg":
model = SparseLogreg(X_train, y_train)
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":
criterion = HeldOutMSE(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
elif model_name == "logreg":
criterion = HeldOutLogistic(X_val,
y_val,
model,
X_test=X_test,
y_test=y_test)
algo = dict_algo[method](criterion)
monitor = Monitor()
if method == 'grid_search':
log_alphas = np.log(np.geomspace(alpha_max, alpha_min, num=100))
grid_search(algo,
None,
None,
monitor,
log_alphas=log_alphas,
tol=tol)
elif method == 'random':
grid_search(algo,
log_alpha_max,
log_alpha_min,
monitor,
tol=tol,
max_evals=n_alphas,
t_max=dict_t_max[dataset_name])
elif method in ("bayesian"):
hyperopt_wrapper(algo,
log_alpha_min,
log_alpha_max,
monitor,
max_evals=n_alphas,
tol=tol,
method='bayesian',
t_max=dict_t_max[dataset_name])
else:
# do line search to find the optimal lambda
grad_search(algo,
log_alpha0,
monitor,
n_outer=n_outer,
tol=tol,
tolerance_decrease=tolerance_decrease,
t_max=dict_t_max[dataset_name])
monitor.times = np.array(monitor.times)
monitor.objs = np.array(monitor.objs)
monitor.objs_test = np.array(monitor.objs_test)
monitor.log_alphas = np.array(monitor.log_alphas)
return (dataset_name, method, tol, n_outer, tolerance_decrease,
monitor.times, monitor.objs, monitor.objs_test, monitor.log_alphas,
norm(y_val), norm(y_test), log_alpha_max, model_name)
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_grid_search():
max_evals = 5
monitor_grid = Monitor()
model = Lasso(estimator=estimator)
criterion = HeldOutMSE(idx_train, idx_train)
alpha_opt_grid, _ = grid_search(criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor_grid,
max_evals=max_evals,
tol=1e-5,
samp="grid")
monitor_random = Monitor()
criterion = HeldOutMSE(idx_train, idx_val)
alpha_opt_random, _ = grid_search(criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor_random,
max_evals=max_evals,
tol=1e-5,
samp="random")
np.testing.assert_allclose(
monitor_random.alphas[np.argmin(monitor_random.objs)],
alpha_opt_random)
np.testing.assert_allclose(
monitor_grid.alphas[np.argmin(monitor_grid.objs)], alpha_opt_grid)
monitor_grid = Monitor()
model = Lasso(estimator=estimator)
criterion = FiniteDiffMonteCarloSure(sigma=sigma_star)
alpha_opt_grid, _ = grid_search(criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor_grid,
max_evals=max_evals,
tol=1e-5,
samp="grid")
monitor_random = Monitor()
criterion = FiniteDiffMonteCarloSure(sigma=sigma_star)
alpha_opt_random, _ = grid_search(criterion,
model,
X,
y,
alpha_min,
alpha_max,
monitor_random,
max_evals=max_evals,
tol=1e-5,
samp="random")
np.testing.assert_allclose(
monitor_random.alphas[np.argmin(monitor_random.objs)],
alpha_opt_random)
np.testing.assert_allclose(
monitor_grid.alphas[np.argmin(monitor_grid.objs)], alpha_opt_grid)
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)
dict_res = {}
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)
def test_grid_search():
max_evals = 5
monitor_grid = Monitor()
model = Lasso(X_train, y_train, estimator=estimator)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Forward()
log_alpha_opt_grid, _ = grid_search(algo,
criterion,
log_alpha_min,
log_alpha_max,
monitor_grid,
max_evals=max_evals,
tol=1e-5,
samp="grid")
monitor_random = Monitor()
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Forward()
log_alpha_opt_random, _ = grid_search(algo,
criterion,
log_alpha_min,
log_alpha_max,
monitor_random,
max_evals=max_evals,
tol=1e-5,
samp="random")
assert (monitor_random.log_alphas[np.argmin(
monitor_random.objs)] == log_alpha_opt_random)
assert (monitor_grid.log_alphas[np.argmin(
monitor_grid.objs)] == log_alpha_opt_grid)
monitor_grid = Monitor()
model = Lasso(X_train, y_train, estimator=estimator)
criterion = SURE(X_train,
y_train,
model,
sigma=sigma_star,
X_test=X_test,
y_test=y_test)
algo = Forward()
log_alpha_opt_grid, _ = grid_search(algo,
criterion,
log_alpha_min,
log_alpha_max,
monitor_grid,
max_evals=max_evals,
tol=1e-5,
samp="grid")
monitor_random = Monitor()
criterion = SURE(X_train,
y_train,
model,
sigma=sigma_star,
X_test=X_test,
y_test=y_test)
algo = Forward()
log_alpha_opt_random, _ = grid_search(algo,
criterion,
log_alpha_min,
log_alpha_max,
monitor_random,
max_evals=max_evals,
tol=1e-5,
samp="random")
assert (monitor_random.log_alphas[np.argmin(
monitor_random.objs)] == log_alpha_opt_random)
assert (monitor_grid.log_alphas[np.argmin(
monitor_grid.objs)] == log_alpha_opt_grid)
