def weighted_c_index_error(target, result):
'''Used in genetic training.
multiplied by length of target array because it is divided by the length of the target array in the genetic algorithm.'''
#len(target) first to compensate for internals in genetic training
#abs( - 0.5) to make both "positive" and "negative" C_index work, since they do
C = get_weighted_C_index(target, result)
return __inversed__(C, len(target))
def testGeneticWeightedCindexError(self):
print("\nWeighted C Error")
T = self.generateRandomTestData(1000)
outputs = self.generateRandomTestData(1000)
c_index = get_weighted_C_index(T, outputs)
rand_error = weighted_c_index_error(T, outputs) / len(T)
test_error = 1 / c_index
print("rand_error = ", rand_error, "test value = ", test_error, "weighted_index = ", c_index)
assert((rand_error - test_error) < 0.0001)
T[:, 0] = np.arange(len(T))
outputs = T
rev_outputs = outputs[::-1]
c_index = get_weighted_C_index(T, outputs)
ord_error = weighted_c_index_error(T, outputs) / len(T)
test_error = 1 / c_index
print("ordered_error = ", ord_error, "test value = ", test_error, "weighted_index = ", c_index)
assert(ord_error == test_error)
c_index = get_weighted_C_index(T, rev_outputs)
rev_error = weighted_c_index_error(T, rev_outputs) / len(T)
#test_error = 1 / c_index #Will give zero-division, set to 9000
test_error = 9000.0
print("reversed_error = ", rev_error, "test value = ", test_error, "weighted_index = ", c_index)
assert(rev_error == test_error)
assert(ord_error < rev_error)
T[:, 0] = np.arange(len(T))
T[0, 1], T[-1, 1] = 1, 1 #Make sure they are non-censored
outputs = T.copy()
outputs[0], outputs[-1] = outputs[-1], outputs[0]
rev_outputs = outputs[::-1]
c_index = get_weighted_C_index(T, outputs)
ord_error = weighted_c_index_error(T, outputs) / len(T)
test_error = 1 / c_index
print("1_off_ends_error = ", ord_error, "test value = ", test_error, "weighted_index = ", c_index)
assert(ord_error == test_error)
assert(ord_error > 1)
ord_error_ends = ord_error
c_index = get_weighted_C_index(T, rev_outputs)
rev_error = weighted_c_index_error(T, rev_outputs) / len(T)
test_error = 1 / c_index
print("1_off_ends_reversed_error = ", rev_error, "test value = ", test_error, "weighted_index = ", c_index)
assert(rev_error == test_error)
assert(rev_error > 1)
rev_error_ends = rev_error
T[:, 0] = np.arange(len(T))
T[len(T) / 2, 1], T[len(T) / 2 + 1, 1] = 1, 1 #Make sure they are non-censored
outputs = T.copy()
outputs[len(T) / 2], outputs[len(T) / 2 + 1] = outputs[len(T) / 2 + 1], outputs[len(T) / 2]
rev_outputs = outputs[::-1]
c_index = get_weighted_C_index(T, outputs)
ord_error = weighted_c_index_error(T, outputs) / len(T)
test_error = 1 / c_index
print("1_off_middle_error = ", ord_error, "test value = ", test_error, "weighted_index = ", c_index)
assert(ord_error == test_error)
assert(ord_error > 1)
assert(ord_error < ord_error_ends)
c_index = get_weighted_C_index(T, rev_outputs)
rev_error = weighted_c_index_error(T, rev_outputs) / len(T)
print("1_off_middle_reversed_error = ", rev_error, "weighted_index = ", c_index)
assert(rev_error > rev_error_ends)
