def test_find_best_parametric_model_works_for_interval_censoring():
T_1 = np.random.exponential(2, 100)
T_2 = T_1 + 1
model, score = utils.find_best_parametric_model((T_1, T_2),
censoring_type="interval",
show_progress=True)
assert True
def test_find_best_parametric_model_works_with_weights_and_entry():
T = np.random.exponential(5, 100)
W = np.random.randint(1, 5, size=100)
entry = np.random.exponential(0.01, 100)
model, score = utils.find_best_parametric_model(T,
weights=W,
entry=entry,
show_progress=True)
assert True
def test_find_best_parametric_model_with_BIC():
T = np.random.exponential(2, 1000)
model, score = utils.find_best_parametric_model(T, scoring_method="BIC")
assert True
def test_find_best_parametric_model_can_accept_other_models():
T = np.random.exponential(2, 1000)
model, score = utils.find_best_parametric_model(
T, additional_models=[ExponentialFitter(),
ExponentialFitter()])
assert True
def test_find_best_parametric_model():
T = np.random.exponential(2, 1000)
E = np.ones_like(T)
model, score = utils.find_best_parametric_model(T, E)
assert True
def test_find_best_parametric_model_works_for_left_censoring():
T = np.random.exponential(2, 100)
model, score = utils.find_best_parametric_model(T, censoring_type="left", show_progress=True)
assert True
