def test_warm_start_l1r_regression():
clf = CDRegressor(warm_start=True, random_state=0, penalty="l1")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
n_nz = clf.n_nonzero()
clf.C = 0.2
clf.fit(bin_dense, bin_target)
n_nz2 = clf.n_nonzero()
assert_true(n_nz < n_nz2)
def test_warm_start_l1r_regression():
clf = CDRegressor(warm_start=True, random_state=0, penalty="l1")
clf.C = 0.1
clf.fit(bin_dense, bin_target)
n_nz = clf.n_nonzero()
clf.C = 0.2
clf.fit(bin_dense, bin_target)
n_nz2 = clf.n_nonzero()
assert_true(n_nz < n_nz2)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
loss="squared", max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
loss="squared", max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
