def test_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=1)
x = model.fit(iterations=50, map_start=False)
assert (model.predict_is(h=5).shape[0] == 5)
def test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=1)
x = model.fit('BBVI', iterations=100, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test2_ppc():
"""
Tests PPC value
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=2)
x = model.fit('BBVI', iterations=100, map_start=False)
p_value = model.ppc(nsims=40)
assert (0.0 <= p_value <= 1.0)
def test2_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=2)
x = model.fit(iterations=50, map_start=False)
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_bbvi_mini_batch():
"""
Tests an ARIMA 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.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=1)
x = model.fit('BBVI', iterations=100, mini_batch=50, map_start=False)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test2_sample_model():
"""
Tests sampling function
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=2)
x = model.fit('BBVI', iterations=100, map_start=False)
sample = model.sample(nsims=40)
assert (sample.shape[0] == 40)
assert (sample.shape[1] == len(data) - 2)
def test2_predict_is_nonconstant():
"""
We should not really have predictions that are constant (should be some difference)...
This captures bugs with the predict function not iterating forward
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=2)
x = model.fit(iterations=50, map_start=False)
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_couple_terms():
"""
Tests an ARIMA model with 1 AR and 1 MA term and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = pf.NNAR(data=data, ar=2, family=pf.Normal(), units=2, layers=1)
x = model.fit(iterations=50, map_start=False)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
