def test_squared_loss(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="squared",
n_estimators=100,
random_state=0)
model.fit(whas500_data.x, whas500_data.y)
time_predicted = model.predict(whas500_data.x)
time_true = whas500_data.y["lenfol"]
event_true = whas500_data.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
assert round(abs(rmse_all - 793.6256945839657), 7) == 0
rmse_uncensored = numpy.sqrt(
mean_squared_error(time_true[event_true],
time_predicted[event_true]))
assert round(abs(rmse_uncensored - 542.83358120153525), 7) == 0
cindex = model.score(whas500_data.x, whas500_data.y)
assert round(abs(cindex - 0.7777082862), 7) == 0
with pytest.raises(
ValueError,
match="`fit` must be called with the loss option set to 'coxph'"
):
model.predict_survival_function(whas500_data.x)
with pytest.raises(
ValueError,
match="`fit` must be called with the loss option set to 'coxph'"
):
model.predict_cumulative_hazard_function(whas500_data.x)
def test_squared_loss(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="squared", n_estimators=100, random_state=0)
model.fit(whas500_data.x, whas500_data.y)
time_predicted = model.predict(whas500_data.x)
time_true = whas500_data.y["lenfol"]
event_true = whas500_data.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
assert round(abs(rmse_all - 793.6256945839657), 7) == 0
rmse_uncensored = numpy.sqrt(mean_squared_error(time_true[event_true], time_predicted[event_true]))
assert round(abs(rmse_uncensored - 542.83358120153525), 7) == 0
cindex = model.score(whas500_data.x, whas500_data.y)
assert round(abs(cindex - 0.7777082862), 7) == 0