def test_sklearn():
for alpha in np.logspace(-3, 3):
def logloss(x):
return logistic._logistic_loss(x, X, y, 0.)
def fprime_logloss(x):
return logistic._logistic_loss_and_grad(x, X, y, 0.)[1]
def g_prox(x, step_size):
"""
L1 regularization
"""
return np.fmax(x - step_size * alpha, 0) - \
np.fmax(- x - step_size * alpha, 0)
clf = logistic.LogisticRegression(penalty='l1',
fit_intercept=False,
C=1 / alpha)
clf.fit(X, y)
opt = fmin_cgprox(logloss,
fprime_logloss,
g_prox,
np.zeros(n_features),
rtol=1e-12)
assert linalg.norm(opt.x - clf.coef_) < 1e-3
def test_optimize():
def logloss(x):
return logistic._logistic_loss(x, X, y, 1.)
def fprime_logloss(x):
return logistic._logistic_loss_and_grad(x, X, y, 1.)[1]
def g_prox(x, alpha):
return x
opt = fmin_cgprox(
logloss, fprime_logloss, g_prox, np.zeros(n_features),
rtol=1e-12)
out = optimize.fmin_l_bfgs_b(
logloss, np.zeros(n_features), fprime=fprime_logloss)
assert linalg.norm(out[0] - opt.x) < 1e-3
def test_optimize():
def logloss(x):
return logistic._logistic_loss(x, X, y, 1.)
def fprime_logloss(x):
return logistic._logistic_loss_and_grad(x, X, y, 1.)[1]
def g_prox(x, alpha):
return x
opt = fmin_cgprox(logloss,
fprime_logloss,
g_prox,
np.zeros(n_features),
rtol=1e-12)
out = optimize.fmin_l_bfgs_b(logloss,
np.zeros(n_features),
fprime=fprime_logloss)
assert linalg.norm(out[0] - opt.x) < 1e-3
def test_sklearn():
for alpha in np.logspace(-3, 3):
def logloss(x):
return logistic._logistic_loss(x, X, y, 0.)
def fprime_logloss(x):
return logistic._logistic_loss_and_grad(x, X, y, 0.)[1]
def g_prox(x, step_size):
"""
L1 regularization
"""
return np.fmax(x - step_size * alpha, 0) - \
np.fmax(- x - step_size * alpha, 0)
clf = logistic.LogisticRegression(
penalty='l1', fit_intercept=False, C=1 / alpha)
clf.fit(X, y)
opt = fmin_cgprox(
logloss, fprime_logloss, g_prox, np.zeros(n_features),
rtol=1e-12)
assert linalg.norm(opt.x - clf.coef_) < 1e-3
