def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=1.0):
min_c = l1_min_c(
X,
y,
loss=loss,
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling,
)
clf = {
"log": LogisticRegression(penalty="l1", solver="liblinear"),
"squared_hinge": LinearSVC(loss="squared_hinge",
penalty="l1",
dual=False),
}[loss]
clf.fit_intercept = fit_intercept
clf.intercept_scaling = intercept_scaling
clf.C = min_c
clf.fit(X, y)
assert (np.asarray(clf.coef_) == 0).all()
assert (np.asarray(clf.intercept_) == 0).all()
clf.C = min_c * 1.01
clf.fit(X, y)
assert (np.asarray(clf.coef_) != 0).any() or (np.asarray(clf.intercept_) !=
0).any()
def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=None):
min_c = l1_min_c(X,
y,
loss=loss,
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling)
clf = {
'log': LogisticRegression(penalty='l1', solver='liblinear'),
'squared_hinge': LinearSVC(loss='squared_hinge',
penalty='l1',
dual=False),
}[loss]
clf.fit_intercept = fit_intercept
clf.intercept_scaling = intercept_scaling
clf.C = min_c
clf.fit(X, y)
assert (np.asarray(clf.coef_) == 0).all()
assert (np.asarray(clf.intercept_) == 0).all()
clf.C = min_c * 1.01
clf.fit(X, y)
assert ((np.asarray(clf.coef_) != 0).any()
or (np.asarray(clf.intercept_) != 0).any())
def test_unsupported_loss():
with pytest.raises(ValueError):
l1_min_c(dense_X, Y1, loss='l1')
def test_ill_posed_min_c():
X = [[0, 0], [0, 0]]
y = [0, 1]
with pytest.raises(ValueError):
l1_min_c(X, y)
def test_l1_min_c_l2_loss():
# loss='l2' should raise ValueError
msg = "loss type not in"
with pytest.raises(ValueError, match=msg):
l1_min_c(dense_X, Y1, loss="l2")
