def test2():
"""
tests the max_kappa function that finds the maximum kappa value for 2 sets
of predictions up to permutations of the cluster numberings
"""
pred1 = np.array([0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0, 0, 0])
pred2 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
x = np.array([[2, 0, 3], [5, 0, 0], [1, 4, 0]])
kappa_max, corrected_x, new_order = max_kappa(pred1, pred2)
assert_allclose(x[:, new_order], corrected_x)
assert kappa_max == kappa_function(x[:, new_order])
def test1():
"""
tests the kappa_function
"""
x = np.array([[2, 0, 3], [5, 0, 0], [1, 4, 0]])
pred1 = np.array([0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 0, 0, 0])
pred2 = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2])
assert_allclose(x, sklearn.metrics.confusion_matrix(pred1, pred2))
# kappa calculation:
# matrix data
# [2,0,3] 5
# [5,0,0] 5
# [1,4,0] 5
# 8 4 3 15
# P_o = 2/15
# P_e = (8*5 + 4*5 + 3*5)/(15^2) = 1/3
# kappa_value = (P_o-P_e)/(1-P_e) = (2/15-1/3)/(1-1/3) = -.299999
assert kappa_function(x) == (2 / 15 - 1 / 3) / (1 - 1 / 3)
