def test_per_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = pf.GPNARX(data=data, ar=2, kernel=pf.Periodic())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_per_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.GPNARX(data=data, ar=2, kernel=pf.Periodic())
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_per_pml():
"""
Tests a PML model estimated with Laplace approximation and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.GPNARX(data=data, ar=1, kernel=pf.Periodic())
x = model.fit('PML')
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_per_mh():
"""
Tests an GPNARX model estimated with Metropolis-Hastings and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.GPNARX(data=data, ar=1, kernel=pf.Periodic())
x = model.fit('M-H', nsims=300)
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_per_bbvi():
"""
Tests an GPNARX model estimated with BBVI and that the length of the latent variable
list is correct, and that the estimated latent variables are not nan
"""
model = pf.GPNARX(data=data, ar=1, kernel=pf.Periodic())
x = model.fit('BBVI', iterations=100)
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_per_couple_terms_integ():
"""
Tests an GPNARX model with 1 AR term, integrated once, and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = pf.GPNARX(data=data, ar=1, integ=1, kernel=pf.Periodic())
x = model.fit()
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)