def test_linearsvx_loss_penalty_deprecations():
X, y = [[0.0], [1.0]], [0, 1]
msg = ("loss='%s' has been deprecated in favor of "
"loss='%s' as of 0.16. Backward compatibility"
" for the %s will be removed in %s")
# LinearSVC
# loss l1 --> hinge
assert_warns_message(FutureWarning,
msg % ("l1", "hinge", "loss='l1'", "0.23"),
svm.LinearSVC(loss="l1").fit, X, y)
# loss l2 --> squared_hinge
assert_warns_message(FutureWarning,
msg % ("l2", "squared_hinge", "loss='l2'", "0.23"),
svm.LinearSVC(loss="l2").fit, X, y)
# LinearSVR
# loss l1 --> epsilon_insensitive
assert_warns_message(
FutureWarning,
msg % ("l1", "epsilon_insensitive", "loss='l1'", "0.23"),
svm.LinearSVR(loss="l1").fit, X, y)
# loss l2 --> squared_epsilon_insensitive
assert_warns_message(
FutureWarning,
msg % ("l2", "squared_epsilon_insensitive", "loss='l2'", "0.23"),
svm.LinearSVR(loss="l2").fit, X, y)
def test_svr():
# Test Support Vector Regression
diabetes = datasets.load_diabetes()
for clf in (svm.NuSVR(kernel='linear', nu=.4,
C=1.0), svm.NuSVR(kernel='linear', nu=.4, C=10.),
svm.SVR(kernel='linear',
C=10.), svm.LinearSVR(C=10.), svm.LinearSVR(C=10.)):
clf.fit(diabetes.data, diabetes.target)
assert clf.score(diabetes.data, diabetes.target) > 0.02
# non-regression test; previously, BaseLibSVM would check that
# len(np.unique(y)) < 2, which must only be done for SVC
svm.SVR().fit(diabetes.data, np.ones(len(diabetes.data)))
svm.LinearSVR().fit(diabetes.data, np.ones(len(diabetes.data)))
def test_linear_svm_convergence_warnings():
# Test that warnings are raised if model does not converge
lsvc = svm.LinearSVC(random_state=0, max_iter=2)
assert_warns(ConvergenceWarning, lsvc.fit, X, Y)
assert lsvc.n_iter_ == 2
lsvr = svm.LinearSVR(random_state=0, max_iter=2)
assert_warns(ConvergenceWarning, lsvr.fit, iris.data, iris.target)
assert lsvr.n_iter_ == 2
def test_linearsvr_fit_sampleweight():
# check correct result when sample_weight is 1
# check that SVR(kernel='linear') and LinearSVC() give
# comparable results
diabetes = datasets.load_diabetes()
n_samples = len(diabetes.target)
unit_weight = np.ones(n_samples)
lsvr = svm.LinearSVR(C=1e3, tol=1e-12,
max_iter=10000).fit(diabetes.data,
diabetes.target,
sample_weight=unit_weight)
score1 = lsvr.score(diabetes.data, diabetes.target)
lsvr_no_weight = svm.LinearSVR(C=1e3, tol=1e-12,
max_iter=10000).fit(diabetes.data,
diabetes.target)
score2 = lsvr_no_weight.score(diabetes.data, diabetes.target)
assert_allclose(np.linalg.norm(lsvr.coef_),
np.linalg.norm(lsvr_no_weight.coef_), 1, 0.0001)
assert_almost_equal(score1, score2, 2)
# check that fit(X) = fit([X1, X2, X3],sample_weight = [n1, n2, n3]) where
# X = X1 repeated n1 times, X2 repeated n2 times and so forth
random_state = check_random_state(0)
random_weight = random_state.randint(0, 10, n_samples)
lsvr_unflat = svm.LinearSVR(C=1e3, tol=1e-12, max_iter=10000).fit(
diabetes.data, diabetes.target, sample_weight=random_weight)
score3 = lsvr_unflat.score(diabetes.data,
diabetes.target,
sample_weight=random_weight)
X_flat = np.repeat(diabetes.data, random_weight, axis=0)
y_flat = np.repeat(diabetes.target, random_weight, axis=0)
lsvr_flat = svm.LinearSVR(C=1e3, tol=1e-12,
max_iter=10000).fit(X_flat, y_flat)
score4 = lsvr_flat.score(X_flat, y_flat)
assert_almost_equal(score3, score4, 2)
def test_linearsvr():
# check that SVR(kernel='linear') and LinearSVC() give
# comparable results
diabetes = datasets.load_diabetes()
lsvr = svm.LinearSVR(C=1e3).fit(diabetes.data, diabetes.target)
score1 = lsvr.score(diabetes.data, diabetes.target)
svr = svm.SVR(kernel='linear', C=1e3).fit(diabetes.data, diabetes.target)
score2 = svr.score(diabetes.data, diabetes.target)
assert_allclose(np.linalg.norm(lsvr.coef_), np.linalg.norm(svr.coef_), 1,
0.0001)
assert_almost_equal(score1, score2, 2)
def test_svr_coef_sign():
# Test that SVR(kernel="linear") has coef_ with the right sign.
# Non-regression test for #2933.
X = np.random.RandomState(21).randn(10, 3)
y = np.random.RandomState(12).randn(10)
for svr in [
svm.SVR(kernel='linear'),
svm.NuSVR(kernel='linear'),
svm.LinearSVR()
]:
svr.fit(X, y)
assert_array_almost_equal(
svr.predict(X),
np.dot(X, svr.coef_.ravel()) + svr.intercept_)