Function test_sparse_decision_function

sklearn/svm/tests/test_sparse.py:207–231

(csr_container)

@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse_decision_function(csr_container):
    # Test decision_function

    # Sanity check, test that decision_function implemented in python
    # returns the same as the one in libsvm

    # multi class:
    iris_data_sp = csr_container(iris.data)
    svc = svm.SVC(kernel="linear", C=0.1, decision_function_shape="ovo")
    clf = svc.fit(iris_data_sp, iris.target)

    dec = safe_sparse_dot(iris_data_sp, clf.coef_.T) + clf.intercept_

    assert_allclose(dec, clf.decision_function(iris_data_sp))

    # binary:
    clf.fit(X, Y)
    dec = np.dot(X, clf.coef_.T) + clf.intercept_
    prediction = clf.predict(X)
    assert_allclose(dec.ravel(), clf.decision_function(X))
    assert_allclose(
        prediction, clf.classes_[(clf.decision_function(X) > 0).astype(int).ravel()]
    )
    expected = np.array([-1.0, -0.66, -1.0, 0.66, 1.0, 1.0])
    assert_array_almost_equal(clf.decision_function(X), expected, decimal=2)


@pytest.mark.parametrize("lil_container", LIL_CONTAINERS)