def test_t_predict_is_length():
"""
Tests that the length of the predict IS dataframe is equal to no of steps h
"""
model = pf.GASReg(formula="y ~ x1", data=data, family=pf.GASt())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_t_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.GASt()) x = model.fit() assert(model.predict_is(h=5).shape[0] == 5)
def test2_t_predict_length():
"""
Tests that the length of the predict dataframe is equal to no of steps h
"""
model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.GASt())
x = model.fit()
x.summary()
assert (model.predict(h=5, oos_data=data_oos).shape[0] == 5)
def test_t_predict_is_nans(): """ Tests that the in-sample predictions are not nans """ model = pf.GASLLT(data=data, family=pf.GASt()) x = model.fit() x.summary() assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def test_t_predict_length(): """ Tests that the prediction dataframe length is equal to the number of steps h """ model = pf.GAS(data=data, ar=2, sc=2, family=pf.GASt()) x = model.fit() x.summary() assert(model.predict(h=5).shape[0] == 5)
def test_t_predict_nans(): """ Tests that the predictions are not nans """ model = pf.GAS(data=data, ar=2, sc=2, family=pf.GASt()) x = model.fit() x.summary() assert(len(model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_t_no_terms():
"""
Tests the length of the latent variable vector for an GASReg model
with no AR or MA terms, and tests that the values are not nan
"""
model = pf.GASReg(formula="y ~ x1", data=data, 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_t_mh():
"""
Tests an GASX model estimated with Metropolis-Hastings, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASt())
x = model.fit('M-H', nsims=300)
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_t_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.GASt())
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_t_couple_terms():
"""
Tests the length of the latent variable vector for an GASX model
with 1 AR and 1 SC term, and tests that the values are not nan
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASt())
x = model.fit()
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_t_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.GASt())
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_t_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=data, family=pf.GASt())
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 test_t_bbvi():
"""
Tests an GASX model estimated with BBVI, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1", data=data, ar=1, sc=1, family=pf.GASt())
x = model.fit('BBVI', iterations=100)
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_t_couple_terms(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLT(data=data, 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 test2_t_predict_is_nans():
"""
Tests that the predictions in-sample are not NaNs
"""
model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.GASt())
x = model.fit()
x.summary()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
def test_t_pml():
"""
Tests an GASReg model estimated with PML, and tests that the latent variable
vector length is correct, and that value are not nan
"""
model = pf.GASReg(formula="y ~ x1", data=data, family=pf.GASt())
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 test2_t_normal():
"""
Tests an GASReg model estimated with Laplace, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = pf.GASReg(formula="y ~ x1 + x2", data=data, family=pf.GASt())
x = model.fit('Laplace')
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_t_predict_nans():
"""
Tests that the predictions are not NaNs
"""
model = pf.GASReg(formula="y ~ x1", data=data, family=pf.GASt())
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_t_no_terms(): """ Tests an GAS 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.GAS(data=data, ar=0, sc=0, family=pf.GASt()) 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_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.GAS(data=data, ar=1, sc=1, integ=1, family=pf.GASt()) 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 test2_t_pml():
"""
Tests an GASX model estimated with PML, with multiple predictors, and
tests that the latent variable vector length is correct, and that value are not nan
"""
model = pf.GASX(formula="y ~ x1 + x2",
data=data,
ar=1,
sc=1,
family=pf.GASt())
x = model.fit('PML')
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 test2_t_no_terms():
"""
Tests the length of the latent variable vector for an GASX model
with no AR or SC terms, and two predictors, and tests that the values
are not nan
"""
model = pf.GASX(formula="y ~ x1 + x2",
data=data,
ar=0,
sc=0,
family=pf.GASt())
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)
