def cohen_kappa_multiclass(actual, predicted):
assert len(actual) == len(predicted)
ac = np.array(actual, dtype=np.float32).reshape((len(actual), -1))
pr = np.array(predicted, dtype=np.float32).reshape((len(predicted), -1))
try:
na = np.argwhere([not np.any(np.isnan(i)) for i in ac]).ravel()
except:
for i in ac:
print(i)
for i in ac:
print(np.any(np.isnan(i)))
if len(na) == 0:
return np.nan
aci = np.argmax(np.array(np.array(ac[na]), dtype=np.int32), axis=1)
pri = np.argmax(np.array(np.array(pr[na]), dtype=np.float32), axis=1)
# for i in range(len(aci)):
# print(aci[i],'--',pri[i],':',np.array(pr[na])[i])
return kpa(aci, pri)
def cohen_kappa(actual, predicted, split=0.5, average_over_labels=True):
assert len(actual) == len(predicted)
ac = np.array(actual, dtype=np.float32).reshape((len(actual), -1))
pr = np.array(predicted, dtype=np.float32).reshape((len(predicted), -1))
na = np.argwhere([not np.any(np.isnan(i)) for i in ac]).ravel()
if len(na) == 0:
return np.nan
ac = np.array(np.array(ac[na]) > split, dtype=np.int32)
pr = np.array(np.array(pr[na]) > split, dtype=np.int32)
label_kpa = []
if hasattr(split, '__iter__'):
assert len(split) == ac.shape[-1]
else:
split = np.ones(ac.shape[1]) * split
for i in range(ac.shape[-1]):
label_kpa.append(
kpa(np.array(np.array(ac[:, i]) > split[i], dtype=np.int32),
np.array(np.array(pr[:, i]) > split[i], dtype=np.int32)))
if average_over_labels:
return np.nanmean(label_kpa)
else:
return label_kpa