def test_gradient(self):
ol = OrderedLogit()
X = np.array([[1.0], [1.0], [1.0]])
y = np.array([1, 1, 2])
coefficients = np.array([1.0, 1.0])
ol.n_attributes = 1
ol.n_classes = 2
ol._prepare_y(y)
expected = np.array([0.5, -0.5])
assert_array_equal(ol._gradient(coefficients, X, y), expected)
def sample_lor():
lor = OrderedLogit(significance=0.9)
lor.alpha_ = np.array([1, 1])
lor.beta_ = np.array([1, 1, 1])
lor._y_dict = {1: 1, 2: 2, 3: 7}
lor.n_attributes = 3
lor.n_classes = 3
lor.N = 10
lor.score_ = 0.9
lor.se_ = np.array([1, 1, 1, 1, 1])
lor.p_values_ = np.array([0, 0, 0, 0, 0])
lor.attribute_names = pd.DataFrame(
['attribute_1', 'attribute_2', 'attribute_3'],
columns=['attribute names'])
return lor