def test_cohens_weighted_kappa(self):
# test basic functionality with full agreement, missing annotations
fa = self.full_agreement
self.assertAlmostEqual(pma.cohens_weighted_kappa(fa.annotations[:,0],
fa.annotations[:,1]),
1.0, 6)
def test_all_invalid(self):
# behavior: all measures should return np.nan for a set of
# annotations with no valid entry
anno = self.invalid_test.annotations
self.assertTrue(
np.isnan(pma.scotts_pi(anno[:, 0], anno[:, 1], nclasses=4)))
self.assertTrue(
np.isnan(pma.cohens_kappa(anno[:, 0], anno[:, 1], nclasses=3)))
self.assertTrue(
np.isnan(
pma.cohens_weighted_kappa(anno[:, 0], anno[:, 1], nclasses=5)))
self.assertTrue(np.isnan(pma.fleiss_kappa(anno, nclasses=4)))
self.assertTrue(np.isnan(pma.krippendorffs_alpha(anno, nclasses=7)))
self.assertTrue(
np.isnan(pmc.pearsons_rho(anno[:, 0], anno[:, 1], nclasses=4)))
self.assertTrue(
np.isnan(pmc.spearmans_rho(anno[:, 0], anno[:, 1], nclasses=4)))
self.assertTrue(np.isnan(pmc.cronbachs_alpha(anno, nclasses=4)))
def test_cohens_weighted_kappa2(self):
# cohen's weighted kappa is the same as cohen's kappa when
# the weights are 0. on the diagonal and 1. elsewhere
anno1 = np.array([0, 0, 1, 2, 1, MV, 3])
anno2 = np.array([0, MV, 1, 0, 1, MV, 2])
weighted_kappa = pma.cohens_weighted_kappa(anno1, anno2,
pmd.binary_distance)
cohens_kappa = pma.cohens_kappa(anno1, anno2)
self.assertAlmostEqual(weighted_kappa, cohens_kappa, 6)
def test_all_invalid(self):
# behavior: all measures should return np.nan for a set of
# annotations with no valid entry
anno = self.invalid_test.annotations
self.assert_(
np.isnan(pma.scotts_pi(anno[:,0], anno[:,1], nclasses=4))
)
self.assert_(
np.isnan(pma.cohens_kappa(anno[:,0], anno[:,1], nclasses=3))
)
self.assert_(
np.isnan(pma.cohens_weighted_kappa(anno[:,0], anno[:,1],
nclasses=5))
)
self.assert_(
np.isnan(pma.fleiss_kappa(anno, nclasses=4))
)
self.assert_(
np.isnan(pma.krippendorffs_alpha(anno, nclasses=7))
)
self.assert_(
np.isnan(pmc.pearsons_rho(anno[:,0], anno[:,1], nclasses=4))
)
self.assert_(
np.isnan(pmc.spearmans_rho(anno[:,0], anno[:,1], nclasses=4))
)
self.assert_(
np.isnan(pmc.cronbachs_alpha(anno, nclasses=4))
)
