def test_debiasing_warm_start():
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1", max_iter=10)
clf.C = 0.5
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 160)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.845)
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1", max_iter=10)
clf.C = 0.500001
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 191)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.97)
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1", max_iter=10,
warm_start=True)
clf.C = 0.5
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 160)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.845)
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1", max_iter=10,
warm_start=True)
clf.C = 0.500001
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 191)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.97)
def test_empty_model():
clf = PrimalSVC(kernel="rbf", gamma=0.1, C=0.1, penalty="l1")
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
clf = PrimalSVC(kernel="rbf", gamma=0.1, C=0.1, penalty="l1l2")
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 0)
acc = clf.score(bin_dense, bin_target)
assert_equal(acc, 0.5)
def test_upper_bound_rbf():
clf = PrimalSVC(random_state=0, penalty="l1", kernel="rbf", gamma=0.1)
Cmin = C_lower_bound(bin_dense, bin_target, kernel="rbf", gamma=0.1)
Cmax = C_upper_bound(bin_dense, bin_target, clf, Cmin, 5.0, 100, 10)
clf.set_params(C=Cmax)
clf.fit(bin_dense, bin_target)
assert_true(clf.n_support_vectors() < 110)
def test_fit_rbf_binary_l2r_kernelized_upper_bound():
clf = PrimalSVC(C=1.0, random_state=0, penalty="l2", loss="squared_hinge",
max_iter=20, kernel="rbf", kernel_regularizer=True,
selection="loss", termination="n_components", n_components=30)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.80)
assert_equal(clf.n_support_vectors(), 30)
clf = PrimalSVC(C=1.0, random_state=0, penalty="l2l2", loss="squared_hinge",
max_iter=20, kernel="rbf", kernel_regularizer=True,
selection="loss", termination="n_components", n_components=30,
warm_debiasing=True)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
assert_almost_equal(acc, 0.80)
assert_equal(clf.n_support_vectors(), 30)
def test_debiasing():
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1l2", C=0.5, Cd=1.0,
max_iter=10)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 160)
assert_almost_equal(clf.score(bin_dense, bin_target), 0.845)
pred = clf.decision_function(bin_dense)
clf = PrimalSVC(kernel="rbf", gamma=0.1, penalty="l1", C=0.5)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 160)
K = pairwise_kernels(bin_dense, clf.support_vectors_, metric="rbf", gamma=0.1)
clf = PrimalLinearSVC(max_iter=10, C=1.0)
clf.fit(K, bin_target)
assert_almost_equal(clf.score(K, bin_target), 0.845)
pred2 = clf.decision_function(K)
assert_array_almost_equal(pred, pred2)
def test_components():
clf = PrimalSVC(random_state=0, penalty="l1", kernel="rbf",
gamma=0.1, C=0.5)
clf.fit(bin_dense, bin_target)
acc = clf.score(bin_dense, bin_target)
clf = PrimalSVC(random_state=0, penalty="l2", kernel="rbf",
gamma=0.1, C=0.5, components=clf.support_vectors_)
clf.fit(bin_dense, bin_target)
assert_equal(clf.n_support_vectors(), 160)
acc2 = clf.score(bin_dense, bin_target)
assert_equal(acc, acc2)
