def test_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def a_test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = ARIMA(data=data, ar=1, ma=0, family=Exponential())
x = model.fit('BBVI', iterations=200, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def a_test_ppc():
"""
Tests PPC value
"""
model = ARIMA(data=data, ar=2, ma=2, family=Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc(nsims=100)
assert (0.0 <= p_value <= 1.0)
def test_ppc():
"""
Tests PPC value
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
p_value = model.ppc(nsims=100)
assert (0.0 <= p_value <= 1.0)
def test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def a_test_sample_model():
"""
Tests sampling function
"""
model = ARIMA(data=data, ar=2, ma=2, family=Exponential())
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_predict_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 = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit()
predictions = model.predict(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_predict_is_nans():
"""
Tests that the in-sample predictions are not nans
"""
model = ARIMA(data=data, ar=2, ma=2)
x = model.fit()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
def test_sample_model():
"""
Tests sampling function
"""
model = ARIMA(data=data, ar=2, ma=2, family=t())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_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 = ARIMA(data=data, ar=1, ma=1)
x = model.fit('PML')
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_mh():
"""
Tests an ARIMA 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 = ARIMA(data=data, ar=1, ma=1)
x = model.fit('M-H', nsims=200, quiet_progress=True)
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = ARIMA(data=data, ar=1, ma=1)
x = model.fit('BBVI',
iterations=100,
quiet_progress=True,
record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[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 = ARIMA(data=data, ar=1, ma=1)
x = model.fit('BBVI', iterations=100, quiet_progress=True, mini_batch=32)
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def a_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 = ARIMA(data=data, ar=1, ma=0, family=Exponential())
x = model.fit('BBVI', iterations=200, mini_batch=32)
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 a_test_laplace():
"""
Tests an ARIMA 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 = ARIMA(data=data, ar=1, ma=1, family=Exponential())
x = model.fit('Laplace')
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 a_test_mh():
"""
Tests an ARIMA 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 = ARIMA(data=data, ar=1, ma=1, family=Exponential())
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_couple_terms_integ():
"""
Tests an ARIMA model with 1 AR and 1 MA term, integrated once, and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = ARIMA(data=data, ar=1, ma=1, integ=1, family=t())
x = model.fit()
assert (len(model.latent_variables.z_list) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_no_terms():
"""
Tests an ARIMA model with no AR or MA terms, and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = ARIMA(data=data, ar=0, ma=0, family=t())
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)
def a_test_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Exponential())
x = model.fit('M-H', nsims=400)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def a_test_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=1, ma=0, family=Exponential())
x = model.fit('BBVI', iterations=200)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def test_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit('M-H', nsims=200, quiet_progress=True)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))
def test_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Poisson())
x = model.fit('BBVI', iterations=100, quiet_progress=True)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >=
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >=
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >=
predictions['1% Prediction Interval'].values))
def test_predict_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMA(data=data, ar=2, ma=2, family=Cauchy())
x = model.fit()
predictions = model.predict(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values >
predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values >
predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values >
predictions['1% Prediction Interval'].values))