def test_both_concordance_index_with_only_censoring_fails_gracefully():
actual_times = np.array([1, 2, 3])
predicted_times = np.array([1, 2, 3])
obs = np.zeros(3)
with pytest.raises(ZeroDivisionError, match="admissable pairs"):
fast_cindex(actual_times, predicted_times, obs)
with pytest.raises(ZeroDivisionError, match="admissable pairs"):
slow_cindex(actual_times, predicted_times, obs)
def test_concordance_index_fast_is_same_as_slow():
size = 100
T = np.random.normal(size=size)
P = np.random.normal(size=size)
C = np.random.choice([0, 1], size=size)
Z = np.zeros_like(T)
# Hard to imagine these failing
assert slow_cindex(T, Z, C) == fast_cindex(T, Z, C)
assert slow_cindex(T, T, C) == fast_cindex(T, T, C)
# This is the real test though
assert slow_cindex(T, P, C) == fast_cindex(T, P, C)
cp = CoxPHFitter()
df = load_rossi()
cp.fit(df, duration_col="week", event_col="arrest")
T = cp.durations.values.ravel()
P = -cp.predict_partial_hazard(df[df.columns.difference(["week", "arrest"])]).values.ravel()
E = cp.event_observed.values.ravel()
assert slow_cindex(T, P, E) == fast_cindex(T, P, E)
def test_concordance_index_function_exits():
N = 10 * 1000
actual_times = np.random.exponential(1, size=N)
predicted_times = np.random.exponential(1, size=N)
obs = np.ones(N)
assert fast_cindex(actual_times, predicted_times, obs)
def test_both_concordance_index_function_deal_with_ties_the_same_way():
actual_times = np.array([1, 1, 2])
predicted_times = np.array([1, 2, 3])
obs = np.ones(3)
assert fast_cindex(actual_times, predicted_times, obs) == slow_cindex(
actual_times, predicted_times, obs) == 1.0