def test_lev_couple_terms():
model = pf.EGARCHM(data=data, p=1, q=1)
model.add_leverage()
x = model.fit()
assert (len(model.latent_variables.z_list) == 7)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_predict_is_intervals():
model = pf.EGARCHM(data=data, q=2, p=2)
x = model.fit()
predictions = model.predict_is(h=10, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values + 0.000001 >= predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values + 0.000001 >= predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values + 0.000001 >= predictions['1% Prediction Interval'].values))
def test_lev_predict_nans():
model = pf.EGARCHM(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_predict_is_nans():
model = pf.EGARCHM(data=data, q=2, p=2)
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_intervals_mh():
model = pf.EGARCHM(data=data, q=1, p=1)
x = model.fit('M-H', nsims=400)
predictions = model.predict_is(h=10, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values + 0.000001 >= predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values + 0.000001 >= predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values + 0.000001 >= predictions['1% Prediction Interval'].values))
def test_lev_bbvi():
model = pf.EGARCHM(data=data, p=1, q=1)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
assert (len(model.latent_variables.z_list) == 7)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_lev_sample_model():
model = pf.EGARCHM(data=data, q=2, p=2)
model.add_leverage()
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_lev_predict_is_intervals_bbvi():
model = pf.EGARCHM(data=data, q=1, p=1)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
predictions = model.predict_is(h=10, intervals=True)
assert(np.all(predictions['99% Prediction Interval'].values + 0.000001 >= predictions['95% Prediction Interval'].values))
assert(np.all(predictions['95% Prediction Interval'].values + 0.000001 >= predictions['5% Prediction Interval'].values))
assert(np.all(predictions['5% Prediction Interval'].values + 0.000001 >= predictions['1% Prediction Interval'].values))
def test_predict_is_nonconstant():
model = pf.EGARCHM(data=data, p=2, q=2)
x = model.fit()
predictions = model.predict_is(h=5, intervals=False)
assert(not np.all(predictions.values==predictions.values[0]))
def test_predict_is_length():
model = pf.EGARCHM(data=data, p=2, q=2)
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_pml():
model = pf.EGARCHM(data=data, p=1, q=1)
x = model.fit('PML')
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_lev_predict_length():
model = pf.EGARCHM(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
x.summary()
assert (model.predict(h=5).shape[0] == 5)
def test_no_terms():
model = pf.EGARCHM(data=data, p=0, q=0)
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_bbvi_mini_batch_elbo():
model = pf.EGARCHM(data=data, p=1, q=1)
x = model.fit('BBVI',iterations=300, map_start=False, mini_batch=32, record_elbo=True)
assert(x.elbo_records[-1]>x.elbo_records[0])
def test_lev_ppc():
model = pf.EGARCHM(data=data, q=2, p=2)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert(0.0 <= p_value <= 1.0)
def test_bbvi_mini_batch():
model = pf.EGARCHM(data=data, p=1, q=1)
x = model.fit('BBVI',iterations=100, map_start=False, mini_batch=32)
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_lev_predict_nonconstant():
model = pf.EGARCHM(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
predictions = model.predict(h=10, intervals=False)
assert(not np.all(predictions.values==predictions.values[0]))
def test_lev_bbvi_elbo():
model = pf.EGARCHM(data=data, p=1, q=1)
model.add_leverage()
x = model.fit('BBVI',iterations=300, map_start=False, record_elbo=True)
assert(x.elbo_records[-1]>x.elbo_records[0])
