def test_cox_cc_runs(numpy):
data = make_dataset(False).apply(lambda x: x.float()).to_numpy()
if not numpy:
data = data.to_tensor()
net = tt.practical.MLPVanilla(data[0].shape[1], [4], 1, False, output_bias=False)
model = CoxPH(net)
fit_model(data, model)
model.compute_baseline_hazards()
assert_survs(data[0], model)
def test_cox_time_runs(numpy):
input, target = make_dataset(False).apply(lambda x: x.float()).to_numpy()
labtrans = CoxTime.label_transform()
target = labtrans.fit_transform(*target)
data = tt.tuplefy(input, target)
if not numpy:
data = data.to_tensor()
net = MLPVanillaCoxTime(data[0].shape[1], [4], False)
model = CoxTime(net)
fit_model(data, model)
model.compute_baseline_hazards()
assert_survs(data[0], model)
def test_pmf_runs(numpy, num_durations):
data = make_dataset(True)
input, target = data
labtrans = PMF.label_transform(num_durations)
target = labtrans.fit_transform(*target)
data = tt.tuplefy(input, target)
if not numpy:
data = data.to_tensor()
net = tt.practical.MLPVanilla(input.shape[1], [4], labtrans.out_features)
model = PMF(net)
fit_model(data, model)
assert_survs(input, model)
model.duration_index = labtrans.cuts
assert_survs(input, model)
cdi = model.interpolate(3, 'const_pdf')
assert_survs(input, cdi)
def test_pc_hazard_runs(numpy, num_durations):
data = make_dataset(True)
input, (durations, events) = data
durations += 1
target = (durations, events)
labtrans = PCHazard.label_transform(num_durations)
target = labtrans.fit_transform(*target)
data = tt.tuplefy(input, target)
if not numpy:
data = data.to_tensor()
net = tt.practical.MLPVanilla(input.shape[1], [4], num_durations)
model = PCHazard(net)
fit_model(data, model)
assert_survs(input, model)
model.duration_index = labtrans.cuts
assert_survs(input, model)