def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=None):
min_c = l1_min_c(X, y, loss, fit_intercept, intercept_scaling)
clf = {
('log', False): LogisticRegression(penalty='l1'),
('log', True): SparseLogRegression(penalty='l1'),
('l2', False): LinearSVC(loss='l2', penalty='l1', dual=False),
('l2', True): SparseSVC(loss='l2', penalty='l1', dual=False),
}[loss, sp.issparse(X)]
clf.fit_intercept = fit_intercept
clf.intercept_scaling = intercept_scaling
clf.C = min_c
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) == 0).all()
assert (np.asanyarray(clf.intercept_) == 0).all()
clf.C = min_c * 1.01
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) != 0).any() or \
(np.asanyarray(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, fit_intercept, intercept_scaling)
clf = {
('log', False): LogisticRegression(penalty='l1'),
('log', True): SparseLogRegression(penalty='l1'),
('l2', False): LinearSVC(loss='l2', penalty='l1', dual=False),
('l2', True): SparseSVC(loss='l2', penalty='l1', dual=False),
}[loss, sp.issparse(X)]
clf.fit_intercept = fit_intercept
clf.intercept_scaling = intercept_scaling
clf.C = min_c
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) == 0).all()
assert (np.asanyarray(clf.intercept_) == 0).all()
clf.C = min_c * 1.01
clf.fit(X, y)
assert (np.asanyarray(clf.coef_) != 0).any() or \
(np.asanyarray(clf.intercept_) != 0).any()
def test_unsupported_loss():
l1_min_c(dense_X, Y1, 'l1')
def test_ill_posed_min_c():
X = [[0, 0], [0, 0]]
y = [0, 1]
l1_min_c(X, y)
def test_unsupported_loss():
l1_min_c(dense_X, Y1, 'l1')
def test_ill_posed_min_c():
X = [[0, 0], [0, 0]]
y = [0, 1]
l1_min_c(X, y)
