def parallel_function(
dataset_name, method, tol=1e-8, n_outer=15):
# load data
X, y = fetch_libsvm(dataset_name)
# subsample the samples and the features
n_samples, n_features = dict_subsampling[dataset_name]
t_max = dict_t_max[dataset_name]
# t_max = 3600
X, y = clean_dataset(X, y, n_samples, n_features)
alpha_max, n_classes = get_alpha_max(X, y)
log_alpha_max = np.log(alpha_max) # maybe to change alpha max value
algo = ImplicitForward(None, n_iter_jac=2000)
estimator = LogisticRegression(
C=1, fit_intercept=False, warm_start=True, max_iter=30, verbose=False)
model = SparseLogreg(estimator=estimator)
idx_train, idx_val, idx_test = get_splits(X, y)
logit_multiclass = LogisticMulticlass(
idx_train, idx_val, algo, idx_test=idx_test)
monitor = Monitor()
if method == "implicit_forward":
log_alpha0 = np.ones(n_classes) * np.log(0.1 * alpha_max)
optimizer = LineSearch(n_outer=100)
grad_search(
algo, logit_multiclass, model, optimizer, X, y, log_alpha0,
monitor)
elif method.startswith(('random', 'bayesian')):
max_evals = dict_max_eval[dataset_name]
log_alpha_min = np.log(alpha_max) - 7
hyperopt_wrapper(
algo, logit_multiclass, model, X, y, log_alpha_min, log_alpha_max,
monitor, max_evals=max_evals, tol=tol, t_max=t_max, method=method,
size_space=n_classes)
elif method == 'grid_search':
n_alphas = 20
p_alphas = np.geomspace(1, 0.001, n_alphas)
p_alphas = np.tile(p_alphas, (n_classes, 1))
for i in range(n_alphas):
log_alpha_i = np.log(alpha_max * p_alphas[:, i])
logit_multiclass.get_val(
model, X, y, log_alpha_i, None, monitor, tol)
monitor.times = np.array(monitor.times).copy()
monitor.objs = np.array(monitor.objs).copy()
monitor.acc_vals = np.array(monitor.acc_vals).copy()
monitor.acc_tests = np.array(monitor.acc_tests).copy()
monitor.log_alphas = np.array(monitor.log_alphas).copy()
return (
dataset_name, method, tol, n_outer, monitor.times, monitor.objs,
monitor.acc_vals, monitor.acc_tests, monitor.log_alphas, log_alpha_max,
n_samples, n_features, n_classes)
# 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))
print("###################### GRID SEARCH ###################")
monitor_grid = Monitor()
for i in range(n_alphas):
n_alphas = 30
p_alphas = np.geomspace(1, 0.0001, n_alphas)
alphas = alpha_max * p_alphas
log_alphas = np.log(alphas)
##############################################################################
# Grid-search
# -----------
print('scikit started')
t0 = time.time()
estimator = LogisticRegression(penalty='l1',
fit_intercept=False,
max_iter=max_iter)
model = SparseLogreg(max_iter=max_iter, estimator=estimator)
criterion = HeldOutLogistic(idx_train, idx_val)
algo_grid = Forward()
monitor_grid = Monitor()
grid_search(algo_grid,
criterion,
model,
X,
y,
log_alpha_min,
log_alpha_max,
monitor_grid,
log_alphas=log_alphas,
tol=tol)
objs = np.array(monitor_grid.objs)
p_alphas = np.geomspace(1, 0.0001, n_alphas)
alphas = alpha_max * p_alphas
log_alphas = np.log(alphas)
##############################################################################
# Grid-search
# -----------
print('scikit started')
t0 = time.time()
estimator = LogisticRegression(penalty='l1',
fit_intercept=False,
solver='saga',
max_iter=max_iter)
model = SparseLogreg(X_train, y_train, max_iter=max_iter, estimator=estimator)
criterion = HeldOutLogistic(X_val, y_val, model)
algo_grid = Forward()
monitor_grid = Monitor()
grid_search(algo_grid,
criterion,
log_alpha_min,
log_alpha_max,
monitor_grid,
log_alphas=log_alphas,
tol=tol)
objs = np.array(monitor_grid.objs)
t_sk = time.time() - t0
print('scikit finished')
def linear_cv(dataset_name, tol=1e-3, compute_jac=True, model_name="lasso"):
X, y = load_libsvm(dataset_name)
X = csc_matrix(X)
n_samples, n_features = X.shape
p_alpha = p_alphas[dataset_name, model_name]
max_iter = max_iters[dataset_name]
if model_name == "lasso":
model = Lasso(X, y, 0, max_iter=max_iter, tol=tol)
elif model_name == "logreg":
model = SparseLogreg(X, y, 0, max_iter=max_iter, tol=tol)
alpha_max = np.exp(model.compute_alpha_max())
alpha = p_alpha * alpha_max
if model_name == "lasso":
clf = Lasso_cel(alpha=alpha,
fit_intercept=False,
warm_start=True,
tol=tol * norm(y)**2 / 2,
max_iter=10000)
clf.fit(X, y)
beta_star = clf.coef_
mask = beta_star != 0
dense = beta_star[mask]
elif model_name == "logreg":
# clf = LogisticRegression(
# penalty='l1', C=(1 / (alpha * n_samples)),
# fit_intercept=False,
# warm_start=True, max_iter=10000,
# tol=tol, verbose=True).fit(X, y)
# clf = LogisticRegression(
# penalty='l1', C=(1 / (alpha * n_samples)),
# fit_intercept=False,
# warm_start=True, max_iter=10000,
# tol=tol, verbose=True,
# solver='liblinear').fit(X, y)
# beta_star = clf.coef_[0]
blitzl1.set_use_intercept(False)
blitzl1.set_tolerance(1e-32)
blitzl1.set_verbose(True)
# blitzl1.set_min_time(60)
prob = blitzl1.LogRegProblem(X, y)
# # lammax = prob.compute_lambda_max()
clf = prob.solve(alpha * n_samples)
beta_star = clf.x
mask = beta_star != 0
mask = np.array(mask)
dense = beta_star[mask]
# if model == "lasso":
v = -n_samples * alpha * np.sign(beta_star[mask])
mat_to_inv = model.get_hessian(mask, dense, np.log(alpha))
# mat_to_inv = X[:, mask].T @ X[:, mask]
jac_temp = cg(mat_to_inv, v, tol=1e-10)
jac_star = np.zeros(n_features)
jac_star[mask] = jac_temp[0]
# elif model == "logreg":
# v = - n_samples * alpha * np.sign(beta_star[mask])
log_alpha = np.log(alpha)
list_beta, list_jac = get_beta_jac_iterdiff(X,
y,
log_alpha,
model,
save_iterates=True,
tol=tol,
max_iter=max_iter,
compute_jac=compute_jac)
diff_beta = norm(list_beta - beta_star, axis=1)
diff_jac = norm(list_jac - jac_star, axis=1)
supp_star = beta_star != 0
n_iter = list_beta.shape[0]
for i in np.arange(n_iter)[::-1]:
supp = list_beta[i, :] != 0
if not np.all(supp == supp_star):
supp_id = i + 1
break
supp_id = 0
return dataset_name, p_alpha, diff_beta, diff_jac, n_iter, supp_id
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)
# X_train, X_val, X_test, y_train, y_val, y_test = get_leukemia()
n_samples, n_features = X_train.shape
print("Starting path computation...")
alpha_max = np.max(np.abs(X_train.T @ (-y_train)))
alpha_max /= (2 * n_samples)
n_alphas = 10
p_alphas = np.geomspace(1, 1e-4, n_alphas)
alphas = p_alphas * alpha_max
log_alphas = np.log(alphas)
tol = 1e-5
# grid search
model = SparseLogreg(X_train, y_train, log_alphas[0], max_iter=1000)
criterion = HeldOutLogistic(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Forward(criterion)
monitor_grid_sk = Monitor()
grid_search(algo, None, None, monitor_grid_sk, log_alphas=log_alphas, tol=tol)
monitor = Monitor()
# grad_search(
# algo, logCs[0], monitor, n_outer=5, verbose=True,
# tolerance_decrease='constant', tol=1e-8,
# t_max=10000)
plt.figure()
plt.plot(monitor_grid_sk.log_alphas, monitor_grid_sk.objs)
plt.plot(monitor.log_alphas, monitor.objs, 'bo')
plt.show(block=False)
log_alpha2 = np.log(alpha_2)
dict_log_alpha = {}
dict_log_alpha["lasso"] = log_alpha
dict_log_alpha["enet"] = np.array([log_alpha1, log_alpha2])
tab = np.linspace(1, 1000, n_features)
dict_log_alpha["wLasso"] = log_alpha + np.log(tab / tab.max())
dict_log_alpha["logreg"] = (log_alpha - np.log(2))
dict_log_alpha["svm"] = 1e-4
dict_log_alpha["svr"] = np.array([1e-2, 1e-2])
# Set models to be tested
models = {}
models["lasso"] = Lasso(estimator=None)
models["enet"] = ElasticNet(estimator=None)
models["wLasso"] = WeightedLasso(estimator=None)
models["logreg"] = SparseLogreg(estimator=None)
models["svm"] = SVM(estimator=None)
models["svr"] = SVR(estimator=None)
custom_models = {}
custom_models["lasso"] = Lasso(estimator=celer.Lasso(
warm_start=True, fit_intercept=False))
custom_models["enet"] = ElasticNet(
estimator=linear_model.ElasticNet(warm_start=True, fit_intercept=False))
custom_models["logreg"] = SparseLogreg(
estimator=celer.LogisticRegression(warm_start=True, fit_intercept=False))
# Compute "ground truth" with cvxpylayer
dict_cvxpy_func = {
'lasso': lasso_cvxpy,
'enet': enet_cvxpy,
n_features=n_features, n_informative=50,
random_state=122, flip_y=0.1, n_redundant=0)
X_val_s = csc_matrix(X_val)
y_train[y_train == 0.0] = -1.0
y_val[y_val == 0.0] = -1.0
alpha_max = np.max(np.abs(X_train.T @ (- y_train)))
alpha_max /= (2 * n_samples)
alpha = 0.3 * alpha_max
log_alpha = np.log(alpha)
tol = 1e-16
models = [
SparseLogreg(
X_train, y_train, max_iter=10000, estimator=None),
SparseLogreg(
X_train_s, y_train, max_iter=10000, estimator=None)
]
estimator = LogisticRegression(
penalty="l1", tol=1e-12, fit_intercept=False, max_iter=100000,
solver="saga")
models_custom = [
SparseLogreg(
X_train, y_train, max_iter=10000, estimator=estimator),
SparseLogreg(
X_train_s, y_train, max_iter=10000, estimator=estimator)
]
flip_y=0.1,
n_redundant=0)
X_s = csc_matrix(X)
y[y == 0.0] = -1.0
idx_train = np.arange(0, 50)
idx_val = np.arange(50, 100)
alpha_max = np.max(np.abs(
X[idx_train, :].T @ y[idx_train])) / (2 * len(idx_train))
alpha = 0.3 * alpha_max
log_alpha = np.log(alpha)
tol = 1e-16
models = [SparseLogreg(max_iter=10000, estimator=None)]
estimator = LogisticRegression(penalty="l1",
tol=1e-12,
fit_intercept=False,
max_iter=100000,
solver="saga")
models_custom = [SparseLogreg(max_iter=10000, estimator=estimator)]
def get_v(mask, dense):
return 2 * (X[np.ix_(idx_val, mask)].T @ (
X[np.ix_(idx_val, mask)] @ dense - y[idx_val])) / len(idx_val)
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, 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]
alpha_max /= 4
log_alpha_max = np.log(alpha_max)
alpha_min = alpha_max * 1e-4
alphas = np.geomspace(alpha_max, alpha_min, 10)
log_alphas = np.log(alphas)
log_alpha0 = 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 = SparseLogreg(
X_train, y_train, max_iter=1000, log_alpha_max=log_alpha_max)
for i in range(size_loop):
monitor = Monitor()
if method == "implicit_forward":
criterion = HeldOutLogistic(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = ImplicitForward(criterion, tol_jac=1e-5, n_iter_jac=100)
_, _, _ = grad_search(
algo=algo, verbose=False,
log_alpha0=log_alpha0, tol=tol,
n_outer=n_outer, monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "forward":
criterion = HeldOutLogistic(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Forward(criterion)
_, _, _ = grad_search(
algo=algo,
log_alpha0=log_alpha0, tol=tol,
n_outer=n_outer, monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "implicit":
criterion = HeldOutLogistic(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Implicit(criterion)
_, _, _ = grad_search(
algo=algo,
log_alpha0=log_alpha0, tol=tol,
n_outer=n_outer, monitor=monitor,
t_max=dict_t_max[dataset_name],
tolerance_decrease=tolerance_decrease)
elif method == "grid_search":
criterion = HeldOutLogistic(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = Forward(criterion)
# log_alpha_min = np.log(alpha_min)
log_alphas = np.log(np.geomspace(alpha_max, alpha_min, num=100))
log_alpha_opt, min_g_func = grid_search(
algo, None, None, monitor, tol=tol, samp="grid",
t_max=dict_t_max[dataset_name], log_alphas=log_alphas)
print(log_alpha_opt)
elif method == "random":
criterion = HeldOutLogistic(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=100, tol=tol, samp="random",
t_max=dict_t_max[dataset_name])
print(log_alpha_opt)
elif method == "lhs":
criterion = HeldOutLogistic(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=100, 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)
