def a_test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.ARIMA(data=data, ar=1, ma=0, family=pf.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_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def a_test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_exponential_predict_length():
"""
Tests that the prediction dataframe length is equal to the number of steps h
"""
model = pf.GAS(data=exponentialdata, ar=2, sc=2, family=pf.Exponential())
x = model.fit()
x.summary()
assert (model.predict(h=5).shape[0] == 5)
def test_exponential_ppc():
"""
Tests PPC value
"""
model = pf.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test_ppc():
"""
Tests PPC value
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc(nsims=100)
assert (0.0 <= p_value <= 1.0)
def a_test_sample_model():
"""
Tests sampling function
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.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_exponential_sample_model():
"""
Tests sampling function
"""
model = pf.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 1)
def test_exponential_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.GAS(data=exponentialdata, ar=2, sc=2, family=pf.Exponential())
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def a_test_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.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit()
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_exponential_bbvi():
"""
Tests an GAS 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.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
x = model.fit('BBVI', iterations=100, map_start=False)
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_exponential_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.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
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 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 = pf.ARIMA(data=data, ar=1, ma=1, family=pf.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_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.ARIMA(data=data, ar=1, ma=0, family=pf.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_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 = pf.ARIMA(data=data, ar=1, ma=1, family=pf.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 a_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 = pf.ARIMA(data=data, ar=0, ma=0, family=pf.Exponential())
x = model.fit()
assert (len(model.latent_variables.z_list) == 1)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_exponential_couple_terms():
"""
Tests an GAS model with 1 AR and 1 SC term and that
the latent variable list length is correct, and that the estimated
latent variables are not nan
"""
model = pf.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
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 test_exponential_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
x = model.fit('BBVI',
iterations=100,
mini_batch=32,
record_elbo=True,
map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def a_test2_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = pf.ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=pf.Exponential())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_exponential_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = pf.GASX(formula="y ~ x1",
data=data,
ar=1,
sc=1,
family=pf.Exponential())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
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.GASX(formula="y ~ x1 + x2",
data=data,
ar=1,
sc=1,
family=pf.Exponential())
assert (not np.all(predictions.values == predictions.values[0]))
def test2_exponential_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = pf.GASX(formula="y ~ x1 + x2",
data=data,
ar=1,
sc=1,
family=pf.Exponential())
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_ppc():
"""
Tests PPC value
"""
model = pf.GASX(formula="y ~ x1",
data=data,
ar=1,
sc=1,
family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test2_ppc():
"""
Tests PPC value
"""
model = pf.ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = pf.ARIMAX(formula="y ~ x1",
data=data,
ar=2,
ma=2,
family=pf.Exponential())
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_exponential_predict_is_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.GAS(data=exponentialdata, ar=1, sc=1, family=pf.Exponential())
x = model.fit()
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_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.ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=1,
ma=1,
family=pf.Exponential())
x = model.fit('BBVI', iterations=200)
predictions = model.predict_is(h=10, fit_method='BBVI', intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def a_test_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.ARIMA(data=data, ar=1, ma=0, family=pf.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 a_test_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.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_bbvi():
"""
Tests an ARIMAX model estimated with BBVI, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = pf.ARIMAX(formula="y ~ x1",
data=data,
ar=1,
ma=1,
family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
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)
