def test_cauchy_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GASLLT(data=data, family=pf.Cauchy())
x = model.fit('BBVI', iterations=100, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test_skewt_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GASLLT(data=data, family=pf.Skewt())
x = model.fit('BBVI',iterations=100, mini_batch=32, record_elbo=True)
assert(x.elbo_records[-1]>x.elbo_records[0])
def test_predict_is_length(): """ Tests that the prediction IS dataframe length is equal to the number of steps h """ model = pf.GASLLT(data=data, family=pf.GASNormal()) x = model.fit() assert(model.predict_is(h=5).shape[0] == 5)
def test_t_ppc():
"""
Tests PPC value
"""
model = pf.GASLLT(data=data, family=pf.t())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test_poisson_predict_is_nans(): """ Tests that the in-sample predictions are not nans """ model = pf.GASLLT(data=countdata, family=pf.GASPoisson()) x = model.fit() x.summary() assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def test_poisson_predict_length(): """ Tests that the prediction dataframe length is equal to the number of steps h """ model = pf.GASLLT(data=countdata, family=pf.GASPoisson()) x = model.fit() x.summary() assert(model.predict(h=5).shape[0] == 5)
def test_predict_nans(): """ Tests that the predictions are not nans """ model = pf.GASLLT(data=data, family=pf.GASNormal()) x = model.fit() x.summary() assert(len(model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_t_sample_model():
"""
Tests sampling function
"""
model = pf.GASLLT(data=data, family=pf.t())
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_t_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.GASLLT(data=data, family=pf.t())
x = model.fit('BBVI', iterations=100, 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 test_laplace_couple_terms_integ(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLT(data=data, integ=1, family=pf.GASLaplace()) 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_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.GASLLT(data=data, family=pf.GASNormal())
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_poisson_laplace():
"""
Tests an GAS 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.GASLLT(data=countdata, family=pf.GASPoisson())
x = model.fit('Laplace')
assert(len(model.latent_variables.z_list) == 2)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert(len(lvs[np.isnan(lvs)]) == 0)
def test_poisson_mh():
"""
Tests an GAS 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.GASLLT(data=countdata, family=pf.GASPoisson())
x = model.fit('M-H',nsims=300)
assert(len(model.latent_variables.z_list) == 2)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert(len(lvs[np.isnan(lvs)]) == 0)
def test_poisson_couple_terms(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLT(data=countdata, family=pf.GASPoisson()) x = model.fit() assert(len(model.latent_variables.z_list) == 2) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0)
def test_laplace_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.GASLLT(data=data, family=pf.GASLaplace())
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_laplace_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.GASLLT(data=data, family=pf.Laplace())
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 test_t_couple_terms_integ(): """ Tests an GAS 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 = pf.GASLLT(data=data, integ=1, family=pf.GASt()) 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)
def test_cauchy_predict_is_intervals():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.GASLLT(data=data, family=pf.Cauchy())
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 test_t_predict_is_intervals_bbvi():
"""
Tests prediction intervals are ordered correctly
"""
model = pf.GASLLT(data=data, family=pf.t())
x = model.fit('BBVI', iterations=100)
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 = pf.GASLLT(data=data, family=pf.Normal())
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))
