def test_oneclass_decision_function():
# Test OneClassSVM decision function
clf = svm.OneClassSVM()
rnd = check_random_state(2)
# Generate train data
X = 0.3 * rnd.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# Generate some regular novel observations
X = 0.3 * rnd.randn(20, 2)
X_test = np.r_[X + 2, X - 2]
# Generate some abnormal novel observations
X_outliers = rnd.uniform(low=-4, high=4, size=(20, 2))
# fit the model
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
clf.fit(X_train)
# predict things
y_pred_test = clf.predict(X_test)
assert np.mean(y_pred_test == 1) > .9
y_pred_outliers = clf.predict(X_outliers)
assert np.mean(y_pred_outliers == -1) > .9
dec_func_test = clf.decision_function(X_test)
assert_array_equal((dec_func_test > 0).ravel(), y_pred_test == 1)
dec_func_outliers = clf.decision_function(X_outliers)
assert_array_equal((dec_func_outliers > 0).ravel(), y_pred_outliers == 1)
def test_sparse_oneclasssvm(datasets_index, kernel):
# Check that sparse OneClassSVM gives the same result as dense OneClassSVM
# many class dataset:
X_blobs, _ = make_blobs(n_samples=100, centers=10, random_state=0)
X_blobs = sparse.csr_matrix(X_blobs)
datasets = [[X_sp, None, T], [X2_sp, None, T2],
[X_blobs[:80], None, X_blobs[80:]],
[iris.data, None, iris.data]]
dataset = datasets[datasets_index]
clf = svm.OneClassSVM(gamma=1, kernel=kernel)
sp_clf = svm.OneClassSVM(gamma=1, kernel=kernel)
check_svm_model_equal(clf, sp_clf, *dataset)
def test_oneclass():
# Test OneClassSVM
clf = svm.OneClassSVM()
clf.fit(X)
pred = clf.predict(T)
assert_array_equal(pred, [1, -1, -1])
assert pred.dtype == np.dtype('intp')
assert_array_almost_equal(clf.intercept_, [-1.218], decimal=3)
assert_array_almost_equal(clf.dual_coef_, [[0.750, 0.750, 0.750, 0.750]],
decimal=3)
with pytest.raises(AttributeError):
(lambda: clf.coef_)()
def test_immutable_coef_property():
# Check that primal coef modification are not silently ignored
svms = [
svm.SVC(kernel='linear').fit(iris.data, iris.target),
svm.NuSVC(kernel='linear').fit(iris.data, iris.target),
svm.SVR(kernel='linear').fit(iris.data, iris.target),
svm.NuSVR(kernel='linear').fit(iris.data, iris.target),
svm.OneClassSVM(kernel='linear').fit(iris.data),
]
for clf in svms:
with pytest.raises(AttributeError):
clf.__setattr__('coef_', np.arange(3))
with pytest.raises((RuntimeError, ValueError)):
clf.coef_.__setitem__((0, 0), 0)
def test_n_support_oneclass_svr():
# Make n_support is correct for oneclass and SVR (used to be
# non-initialized)
# this is a non regression test for issue #14774
X = np.array([[0], [0.44], [0.45], [0.46], [1]])
clf = svm.OneClassSVM()
assert not hasattr(clf, 'n_support_')
clf.fit(X)
assert clf.n_support_ == clf.support_vectors_.shape[0]
assert clf.n_support_.size == 1
assert clf.n_support_ == 3
y = np.arange(X.shape[0])
reg = svm.SVR().fit(X, y)
assert reg.n_support_ == reg.support_vectors_.shape[0]
assert reg.n_support_.size == 1
assert reg.n_support_ == 4
def test_oneclass_score_samples():
X_train = [[1, 1], [1, 2], [2, 1]]
clf = svm.OneClassSVM(gamma=1).fit(X_train)
assert_array_equal(clf.score_samples([[2., 2.]]),
clf.decision_function([[2., 2.]]) + clf.offset_)