def test_fit_rbf_binary():
for selection in ("permute", "active", "loss"):
clf = LaSVM(random_state=0, max_iter=2, kernel="rbf",
selection=selection)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
def test_fit_linear_binary():
for selection, exp in (("permute", 1.0),
("active", 1.0),
("loss", 1.0)):
clf = LaSVM(random_state=0, max_iter=2, kernel="linear",
selection=selection)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, exp)
def test_warm_start():
for selection in ("permute", "active", "loss"):
clf = LaSVM(random_state=0, max_iter=2, kernel="rbf", warm_start=True, selection=selection)
clf.C = 0.5
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0, 1)
clf.C = 0.6
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 1.0)
def test_sv_upper_bound():
clf = LaSVM(random_state=0, max_iter=2, kernel="rbf", finish_step=True,
termination="n_sv", sv_upper_bound=30)
clf.fit(bin_dense, bin_target)
n_sv = np.sum(clf.dual_coef_ != 0)
assert_equal(n_sv, 30)
def test_fit_rbf_multi():
clf = LaSVM(kernel="rbf", gamma=0.1, random_state=0)
clf.fit(mult_dense, mult_target)
y_pred = clf.predict(mult_dense)
acc = np.mean(y_pred == mult_target)
assert_almost_equal(acc, 1.0)
def test_n_components():
clf = LaSVM(random_state=0, max_iter=2, kernel="rbf", finish_step=True, termination="n_components", n_components=30)
clf.fit(bin_dense, bin_target)
n_sv = np.sum(clf.coef_ != 0)
assert_equal(n_sv, 30)
