def test_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2)
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2)
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_2_ppc():
"""
Tests PPC value
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2)
x = model.fit('BBVI', iterations=100, quiet_progress=True)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test_predict_is_nans():
"""
Tests that the predictions in-sample are not NaNs
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Laplace())
x = model.fit()
x.summary()
assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def 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 = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1, family=Poisson())
x = model.fit('BBVI', iterations=200, quiet_progress=True)
predictions = model.predict_is(h=10, fit_method='BBVI', intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_2_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 = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2)
x = model.fit()
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_2_sample_model():
"""
Tests sampling function
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2)
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_2_predict_nans():
"""
Tests that the predictions are not NaNs
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Laplace())
x = model.fit()
x.summary()
assert(len(model.predict(h=5, oos_data=data_oos).values[np.isnan(model.predict(h=5,
oos_data=data_oos).values)]) == 0)
def test_pml():
"""
Tests an ARIMAX model estimated with PML, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1)
x = model.fit('PML')
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_mh():
"""
Tests an ARIMAX model estimated with Metropolis-Hastings, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1)
x = model.fit('M-H', nsims=200, quiet_progress=True)
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 the length of the latent variable vector for an ARIMAX model
with no AR or MA terms, and tests that the values are not nan
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=0, ma=0, family=Laplace())
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_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1)
x = model.fit('BBVI',
iterations=200,
record_elbo=True,
quiet_progress=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test_couple_terms():
"""
Tests the length of the latent variable vector for an ARIMAX model
with 1 AR and 1 MA term, and tests that the values are not nan
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=1, ma=1)
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_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 = ARIMAX(formula="y ~ x1", 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) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_2_bbvi():
"""
Tests an ARIMAX model estimated with BBVI, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=1, ma=1)
x = model.fit('BBVI', iterations=100, quiet_progress=True)
assert (len(model.latent_variables.z_list) == 6)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_2_laplace():
"""
Tests an ARIMAX model estimated with Laplace, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=1, ma=1)
x = model.fit('Laplace')
assert (len(model.latent_variables.z_list) == 6)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_2_predict_is_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Laplace())
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[model.data_name].values))
assert(np.all(predictions[model.data_name].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 = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=t())
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[model.data_name].values))
assert(np.all(predictions[model.data_name].values > predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def a_test2_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=Exponential())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def a_test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = ARIMAX(formula="y ~ x1",
data=data,
ar=1,
ma=1,
family=Exponential())
x = model.fit('BBVI', iterations=300, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test_2_predict_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1 + x2", data=data, ar=2, ma=2, family=Laplace())
x = model.fit()
predictions = model.predict(h=10, oos_data=data_oos, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values > predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values > predictions[model.data_name].values))
assert(np.all(predictions[model.data_name].values > predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values > predictions['1% Prediction Interval'].values))
def a_test2_ppc():
"""
Tests PPC value
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=2,
ma=2,
family=Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test2_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 = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=1,
ma=1,
family=Exponential())
x = model.fit('BBVI', iterations=200)
predictions = model.predict(h=10, oos_data=data_oos, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def a_test2_sample_model():
"""
Tests sampling function
"""
model = ARIMAX(formula="y ~ x1 + x2",
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_is_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Poisson())
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_pml():
"""
Tests an ARIMAX model estimated with PML, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1 + x2",
data=data,
ar=1,
ma=1,
family=Exponential())
x = model.fit('PML')
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 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 = ARIMAX(formula="y ~ x1",
data=data,
ar=1,
ma=1,
family=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)
def test_predict_intervals_mh():
"""
Tests prediction intervals are ordered correctly
"""
model = ARIMAX(formula="y ~ x1", data=data, ar=2, ma=2, family=Poisson())
x = model.fit('M-H', nsims=200, quiet_progress=True)
predictions = model.predict(h=10, oos_data=data_oos, 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_mh():
"""
Tests an ARIMAX model estimated with Metropolis-Hastings, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = ARIMAX(formula="y ~ x1",
data=data,
ar=1,
ma=1,
family=Exponential())
x = model.fit('M-H', nsims=300)
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_couple_terms_integ():
"""
Tests the length of the latent variable vector for an ARIMAX model
with 1 AR and 1 MA term and integrated once, and tests that the
values are not nan
"""
model = ARIMAX(formula="y ~ x1",
data=data,
ar=1,
ma=1,
integ=1,
family=Exponential())
x = model.fit()
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)
