def test_decision_function_shape():
X, y = make_blobs(n_samples=80, centers=5, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# check shape of ovo_decition_function=True
clf = SVC(kernel='linear',
decision_function_shape='ovo').fit(X_train, y_train)
dec = clf.decision_function(X_train)
assert dec.shape == (len(X_train), 10)
def test_decision_function(queue):
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]], dtype=np.float32)
Y = np.array([1, 1, 1, 2, 2, 2], dtype=np.float32)
clf = SVC(kernel='rbf', gamma=1, decision_function_shape='ovo')
clf.fit(X, Y, queue=queue)
rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
assert_array_almost_equal(dec.ravel(), clf.decision_function(X, queue=queue))
def test_decision_function():
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
Y = [1, 1, 1, 2, 2, 2]
clf = SVC(kernel='rbf', gamma=1, decision_function_shape='ovo')
clf.fit(X, Y)
rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
def test_pickle(queue):
iris = datasets.load_iris()
clf = SVC(kernel='linear').fit(iris.data, iris.target, queue=queue)
expected = clf.decision_function(iris.data, queue=queue)
import pickle
dump = pickle.dumps(clf)
clf2 = pickle.loads(dump)
assert type(clf2) == clf.__class__
result = clf2.decision_function(iris.data, queue=queue)
assert_array_equal(expected, result)
def test_decision_function_shape(queue):
X, y = make_blobs(n_samples=80, centers=5, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# check shape of ovo_decition_function=True
clf = SVC(kernel='linear',
decision_function_shape='ovo').fit(X_train, y_train, queue=queue)
dec = clf.decision_function(X_train, queue=queue)
assert dec.shape == (len(X_train), 10)
with pytest.raises(ValueError, match="must be either 'ovr' or 'ovo'"):
SVC(decision_function_shape='bad').fit(X_train, y_train, queue=queue)
